September 2014 

NASA/TM2014-218528 

NESC-RP-13-00847 

 

Flight Testing of the Space Launch System 

(SLS) Adaptive Augmenting Control (AAC) 

Algorithm on an F/A-18

Cornelius J. Dennehy/NESC and Tannen S. VanZwieten/NESC 

Langley Research Center, Hampton, Virginia 

Curtis E. Hanson 

Armstrong Flight Research Center, Edwards Air Force Base, California 

John H. Wall 

Jacobs Technology, Huntsville, Alabama 

Chris J. Miller 

Armstrong Flight Research Center, Edwards Air Force Base, California 

Eric T. Gilligan 

Marshall Space Flight Center, Huntsville, Alabama 

Jeb S. Orr 

Jacobs Technology, Huntsville, Alabama 
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September 2014 

NASA/TM2014-218528 

NESC-RP-13-00847 

 

Flight Testing of the Space Launch System 

(SLS) Adaptive Augmenting Control (AAC) 

Algorithm on an F/A-18

Cornelius J. Dennehy/NESC and Tannen S. VanZwieten/NESC 

Langley Research Center, Hampton, Virginia 

Curtis E. Hanson 

Armstrong Flight Research Center, Edwards Air Force Base, California 

John H. Wall 

Jacobs Technology, Huntsville, Alabama 

Chris J. Miller 

Armstrong Flight Research Center, Edwards Air Force Base, California 

Eric T. Gilligan 

Marshall Space Flight Center, Huntsville, Alabama 

Jeb S. Orr 

Jacobs Technology, Huntsville, Alabama 
Available from: 

NASA Center for AeroSpace Information 

7115 Standard Drive 

Hanover, MD 21076-1320

443-757-5802 

Acknowledgments 

In addition to the aforementioned team members, the authors would like to extend 

appreciation to those who have supported the initial and continued development 

of the AAC algorithm. This includes Mark Whorton, former Guidance, 

Navigation, and Mission Analysis Branch Chief at MSFC, for casting the vision 

and guiding the initial research toward the development of adaptive control for 

launch vehicles; the late David L. Mellen, Honeywell X-15 Project Engineer, who 

laid an early vision for forward gain adaptive control and whose published work 

helped to form the current structure of the SLS AAC technology; and Charles 

Hall, SLS GN&C lead at MSFC, who has been critical in advocating the 

maturation and inclusion of AAC, when appropriate, as part of the SLS baseline 

autopilot design. Several other members of the NASA community also served key 

roles in allocating time during the algorithm’s initial development phase, 

providing expert review, gaining support within the NASA community, and 

obtaining resources toward the flight characterization experiment, including 

MSFC line managers Don Krupp, Mark Jackson, Stephen Ryan, and David 

Edwards, as well as SLS managers and engineers Jimmy Compton, Jiann-Woei 

(Jimmy) Jang, John Hanson, Mark West, and Garry Lyles. 

The team would like to thank engineers Nhan Nguyen at Ames Research Center 

(ARC), Irene Gregory at Langley Research Center (LaRC), and John Bosworth 

and John Burken at Armstrong Flight Research Center (AFRC) for participating 

in the peer review of the flight experiment. The support of David Voracek, AFRC 

Chief Technologist, is also appreciated for providing resources that allowed 

AFRC engineers to complete the initial advocacy and the final reporting of the 

experiment. 

The team is also grateful for the funding partnership between the NESC and the 

SLS Program, AFRC Technical Excellence, and the STMD GCDP, toward the 

completion of this flight campaign. 

The use of trademarks or names of manufacturers in the report is for accurate reporting and does not 

constitute an official endorsement, either expressed or implied, of such products or manufacturers by the 

National Aeronautics and Space Administration.
NASA Engineering and Safety Center

Technical Assessment Report

Document #:

NESC-RP13-00847 

Version:

1.0 

Title:

Flight Testing of the SLS Launch Vehicle AAC 

Algorithm on an F/A-18

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Flight Testing of the Space Launch System (SLS) 

Adaptive Augmenting Control (AAC) 

Algorithm on an F/A-18

Volume 1

July 31, 2014

NESC Request No.: TI-13-00847
NASA Engineering and Safety Center

Technical Assessment Report

Document #:

NESC-RP13-00847 

Version:

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Title:

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Algorithm on an F/A-18

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Report Approval and Revision History

NOTE: This document was approved at the July 31, 2014, NRB. This document was submitted 

to the NESC Director on August 28, 2014, for configuration control. 

Approved:

NESC Director Date

Version Description of Revision Office of Primary 

Responsibility Effective Date

1.0 Initial Release Mr. Cornelius J. 

Dennehy, NASA 

Technical Fellow for 

GN&C, GSFC

7/31/14 

NESC Request No.: TI-13-00847

Original Signature on File 8/29/14
NASA Engineering and Safety Center

Technical Assessment Report

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Table of Contents

Technical Assessment Report

1.0 Notification and Authorization ..................................................................................................... 6

2.0 Signature Page ................................................................................................................................ 7

3.0 Team List ........................................................................................................................................ 8

3.1 Acknowledgements ............................................................................................................. 9

4.0 Executive Summary ..................................................................................................................... 10

5.0 Assessment Plan ........................................................................................................................... 11

6.0 Background .................................................................................................................................. 12

6.1 Motivation for Advanced Control ..................................................................................... 14

6.2 Overview of the SLS AAC Algorithm .............................................................................. 15

6.3 Motivation for Flight Testing............................................................................................ 18

6.3.1 TRL Assessment ............................................................................................................... 18

6.4 Objectives of the Research Flights ................................................................................... 22

7.0 Construction of Dynamically Relevant Research Flights ......................................................... 22

7.1 Applicability of the F/A-18 as an SLS Flight Test Platform ............................................ 23

7.1.1 Description of the F/A-18 FAST Platform ....................................................................... 26

7.2 Experiment Trajectory ...................................................................................................... 28

7.3 Nonlinear Dynamic Inversion to Achieve Pitch Attitude Matching ................................. 30

7.4 SLS Reference Dynamics ................................................................................................. 31

8.0 Flight Control Implementation and Test Scenarios .................................................................. 35

8.1 SLS FCS Overview ........................................................................................................... 35

8.2 Flight Software Prototype Implementation ....................................................................... 37

8.3 Description of SLS Scenarios Implemented During Flight .............................................. 39

9.0 Summary of Research Flights and Results ................................................................................ 41

9.1 Flight Test Results for Near-nominal SLS Scenarios (Objective 1) ................................. 41

9.2 Flight Test Results with Low Performance SLS Scenarios (Objective 2) ........................ 42

9.3 Flight Test Results with Unstable Parasitic Dynamics (Objective 3) ............................... 44

9.4 Airframe Structural Mode Identification and Amplification Test (Objective 3) .............. 47

9.5 Evaluation of Manual Steering Mode (Objective 4) ......................................................... 49

9.5.1 Objective 1 TC with Manual Steering .............................................................................. 51

9.5.2 Objective 2 TCs with Manual Steering ............................................................................. 52

9.5.3 Objective 3 TCs with Manual Steering ............................................................................. 52

9.6 Summary of Results .......................................................................................................... 53

10.0 Findings, Observations, and NESC Recommendations............................................................ 54

10.1 Findings ............................................................................................................................ 54

10.2 Observations ..................................................................................................................... 54

10.3 NESC Recommendations ................................................................................................. 55

NESC Request No.: TI-13-00847
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11.0 Alternate Viewpoint ..................................................................................................................... 55

12.0 Other Deliverables ....................................................................................................................... 55

13.0 Lessons Learned ........................................................................................................................... 55

14.0 Recommendations for NASA Standards and Specifications .................................................... 56

15.0 Definition of Terms ...................................................................................................................... 56

16.0 Acronym List ................................................................................................................................ 57

17.0 References ..................................................................................................................................... 59

18.0 Appendices (separate Volume 2, Parts 1 and 2) ........................................................................ 61

List of Figures

Figure 5.0-1. Key Milestones for Assessment and the SLS Program ..................................................... 11

Figure 6.0-1. SLS Block I Vehicle ......................................................................................................... 13

Figure 6.0-2. F/A-18 FAST .................................................................................................................... 14

Figure 6.2-1. The AAC Algorithm Design Paradigm is to Adapt on an As-needed Basis ..................... 16

Figure 6.2-2. Block Diagram of the SLS FCS with AAC ...................................................................... 16

Figure 6.3-1. Flight Testing of the SLS Autopilot Flight Software Prototype ....................................... 18

Figure 6.3-2. AAC TRL 7 Justification: After F/A-18 Flights ............................................................... 20

Figure 6.3-3. AAC TRL 6 Justification: After F/A-18 Flights ............................................................... 21

Figure 7.1-1. Class B Envelope and Sample Trajectory ......................................................................... 23

Figure 7.1-2. F/A-18 in Flight ................................................................................................................ 26

Figure 7.1-3. F/A-18 Profile ................................................................................................................... 26

Figure 7.1-4. RFCS/ARTS ..................................................................................................................... 27

Figure 7.2-1. Prescribed Trajectory for the Flight Experiment .............................................................. 29

Figure 7.2-2. Photographs Depicting the Trajectory Flown Repeatedly by the F/A-18 ......................... 30

Figure 7.4-1. LVAC Launch Vehicle Reference Model Architecture .................................................... 33

Figure 7.4-2. Reference Model Wind Profiles: Baseline (left) and Shear (right) ................................... 35

Figure 8.1-1. SLS FCS Overview ........................................................................................................... 36

Figure 8.2-1. Comparison of Pitch Rate Filter after Reoptimization at Target Update Rate ................. 39

Figure 9.1-1. Adaptation of the Total Loop Gain (nominal SLS scenario) ............................................ 42

Figure 9.1-2. Results for the Nominal SLS Scenario ............................................................................. 42

Figure 9.2-1. Adaptation of the Total Loop Gain (low performance scenario) ...................................... 44

Figure 9.2-2. Low Performance Results ................................................................................................. 44

Figure 9.3-1. Adaptation of the Total Loop Gain (fictitiously unstable, flexible body dynamics) ........ 46

Figure 9.3-2. Results for the Fictitiously Unstable Flexible Body Dynamics ........................................ 46

Figure 9.4-1. F/A-18 Airframe Mode Reconstructed Frequency Response ........................................... 48

Figure 9.4-2. Airframe Resonance Amplification Filter ........................................................................ 48

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Figure 9.4-3. Airframe Structural Mode Recovery TC Results .............................................................. 49

Figure 9.5-1. Manual Steering Mode Implementation ........................................................................... 50

List of Tables

Table 8.3-1. Full Set of Test Cases Generated for Research Flights ..................................................... 39

Table 9.1-1. Flight-tested Objective 1 Scenarios/Test Cases ................................................................ 41

Table 9.2-1. Flight-tested Objective 2 Scenarios/Test Cases ................................................................ 43

Table 9.3-1. Flight-tested Objective 3 Scenarios/Test Cases ................................................................ 45

Table 9.5-1. Manual Steering Mode Test Matrix .................................................................................. 51

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Technical Assessment Report

1.0 Notification and Authorization

The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed 

an adaptive augmenting control (AAC) algorithm for launch vehicles that improves robustness 

and performance on an as-needed basis by adapting a classical control algorithm to unexpected 

environments or variations in vehicle dynamics. This was baselined as part of the Space Launch 

System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was 

asked to partner with the SLS Program and the Space Technology Mission Directorate (STMD)

Game Changing Development Program (GCDP) to flight test the AAC algorithm on a manned 

aircraft that can achieve a high level of dynamic similarity to a launch vehicle and raise the 

technology readiness of the algorithm early in the program. 

Mr. Cornelius J. Dennehy, NASA Technical Fellow for Guidance, Navigation, and Control 

(GN&C), was selected as the NESC lead, with Dr. Tannen VanZwieten assigned as the 

technical lead. The assessment plan was approved by the NESC Review Board (NRB) on 

January 17, 2013. 

The key stakeholders for this assessment were Mr. Garry Lyles, SLS Program Chief Engineer; 

Mr. Kurt Jackson, SLS Program Integrated Avionics and Software Discipline Lead Engineer; 

and Mr. Mark West, SLS Vehicle Management Discipline Lead Engineer. 

NESC Request No.: TI-13-00847
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Algorithm on an F/A-18

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2.0 Signature Page

Submitted by: 

Mr. Cornelius J. Dennehy Date

Significant Contributors: 

Dr. Tannen S. VanZwieten Date Mr. Curtis E. Hanson Date

Mr. John H. Wall Date Mr. Chris J. Miller Date

Mr. Eric T. Gilligan Date Dr. Jeb S. Orr Date

Signatories declare the findings, observations, and NESC recommendations compiled in the 

report are factually based from data extracted from program/project documents, contractor 

reports, and open literature, and/or generated from independently conducted tests, analyses, and 

inspections. 

NESC Request No.: TI-13-00847

Team Signature Page on File - 9/11/14
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3.0 Team List

Name Discipline Organization

Core Team

Neil Dennehy NESC Lead GSFC

Tannen VanZwieten Technical Lead (Controls) MSFC

Chris Miller F/A-18 Test Chief Engineer AFRC

Patricia Pahlavani MTSO Program Analyst LaRC

Flight Test Support

Curtis Hanson Controls AFRC

James Lee F/A-18 Test Chief Engineer 

(Retired) AFRC

Oscar Murillo Controls AFRC

Richard Larson Systems Lead AFRC

Dan Goodrick Instrumentation Lead AFRC

Richard Weaver Safety AFRC

James Less Pilot AFRC

David Larson Pilot AFRC

Brian Griffin Operations Lead AFRC

William Fredricksen Avionics Lead AFRC

Otis Allen Avionics AFRC

Donald Warren Crew Chief AFRC

Donald Herman Mechanic AFRC

Marlin Picket Simulation Lead AFRC

Karla Shy Control Room AFRC

John Carter Project Manager AFRC

John Kelly Project Manager AFRC

Scott Erickson Quality Assurance AFRC

Bob Perry Program Analyst AFRC

SLS Algorithm Development

Eric Gilligan Algorithm Development MSFC

Jeb Orr Algorithm Development MSFC/The Charles Stark 

Draper Laboratory

John Wall Algorithm Development MSFC/Dynamic 

Concepts

Consultants

Victoria Regenie NESC Systems Engineering Office AFRC

Kenneth Lebsock NESC GN&C Technical Discipline 

Deputy Lead GSFC/Orbital Sciences

Daniel Murri NASA Technical Fellow for Flight 

Mechanics LARC

James Stewart NESC Chief Engineer at AFRC AFRC

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Name Discipline Organization

Steven Gentz NESC Chief Engineer at MSFC MSFC

Don Krupp Control Systems Design and

Analysis Branch Chief MSFC

Charles Hall SLS GN&C Lead MSFC

Steve Derry Controls/PTI Waveform

Development LaRC

Administrative Support

Linda Burgess Planning and Control Analyst LaRC/AMA

Jonay Campbell Technical Writer LaRC/NG

Pamela Sparks Project Coordinator LaRC/AMA

3.1 Acknowledgements

In addition to the aforementioned team members, the authors would like to extend appreciation 

to those who have supported the initial and continued development of the AAC algorithm. This 

includes Mark Whorton, former Guidance, Navigation, and Mission Analysis Branch Chief at 

MSFC, for casting the vision and guiding the initial research toward the development of adaptive 

control for launch vehicles; the late David L. Mellen, Honeywell X-15 Project Engineer, who 

laid an early vision for forward gain adaptive control and whose published work helped to form 

the current structure of the SLS AAC technology; and Charles Hall, SLS GN&C lead at MSFC, 

who has been critical in advocating the maturation and inclusion of AAC, when appropriate, as 

part of the SLS baseline autopilot design. Several other members of the NASA community also 

served key roles in allocating time during the algorithm’s initial development phase, providing 

expert review, gaining support within the NASA community, and obtaining resources toward the 

flight characterization experiment, including MSFC line managers Don Krupp, Mark Jackson, 

Stephen Ryan, and David Edwards, as well as SLS managers and engineers Jimmy Compton, 

Jiann-Woei (Jimmy) Jang, John Hanson, Mark West, and Garry Lyles. 

The team would like to thank engineers Nhan Nguyen at Ames Research Center (ARC), 

Irene Gregory at Langley Research Center (LaRC), and John Bosworth and John Burken at 

Armstrong Flight Research Center (AFRC) for participating in the peer review of the flight 

experiment. The support of David Voracek, AFRC Chief Technologist, is also appreciated for 

providing resources that allowed AFRC engineers to complete the initial advocacy and the final 

reporting of the experiment.

The team is also grateful for the funding partnership between the NESC and the SLS Program, 

AFRC Technical Excellence, and the STMD GCDP, toward the completion of this flight 

campaign. 

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4.0 Executive Summary

The NASA Engineering and Safety Center (NESC) was asked to partner with the Space Launch 

System (SLS) Program and the Space Technology Mission Directorate’s Game Changing 

Development Program (GCDP) to flight test the SLS adaptive augmenting control (AAC) 

algorithm on a piloted Fighter/Attack (F/A)-18. The AAC algorithm, developed within the 

Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division, augments the 

fixed-gain portion of the SLS flight control system (FCS) in order to maximize launch vehicle 

survivability and crew safety. AAC, also referred to herein as launch vehicle adaptive control 

(LVAC), was baselined as part of the FCS design but was the only element that had not yet been 

flight tested. 

With the unique capabilities of its Airborne Research Test System (ARTS) and a previously 

certified nonlinear dynamic inversion (NDI) controller, the F/A-18 Full-Scale Advanced Systems 

Testbed (FAST) aircraft was able to match the SLS model attitude error dynamics during flight. 

This capability allowed the SLS FCS, including the AAC component but excluding the 

disturbance compensation algorithm (DCA), to be applied as the control system for the F/A-18 

so that its performance could be demonstrated in a flight environment.

A series of research flights were conducted in November and December of 2013, which verified 

that the AAC algorithm functionally met its design objectives through multiple SLS scenarios 

and over a hundred SLS-like trajectories. This included verification of the ability of the SLS 

FCS to recover from adverse flight conditions using adaptation since many of the simulated 

flight conditions could not be suitably accommodated in the absence of the adaptive algorithm. 

The flight tests also explored interactions between manual steering and the AAC. Although the 

research flights demonstrated that AAC can aid the vehicle’s ability to retain stable flight control 

and limit flight loads during certain adverse scenarios, the use of AAC as-implemented when 

manual steering is engaged can increase the probability of an adverse pilot-vehicle interaction. 

Thus, if a manual steering mode is used in future SLS missions, design changes or fade-out of 

the adaptive component upon engagement may be needed. 

The success of the flight tests prior to the SLS Program’s Critical Design Review (CDR) raised 

the technology readiness level (TRL) of the AAC algorithm to level 7, “System prototype 

demonstration in an operational environment.” This demonstration of AAC also increased 

internal and external confidence in the algorithm readiness for the first unmanned flight test of 

the SLS. 

This collaborative engineering/flight test effort represented not only a significant contribution to 

the SLS Program but also aligned with one of the top technical challenges of the guidance, 

navigation, and control (GN&C) discipline. This challenge is for NASA to investigate and gain 

hands-on experience with “modern” control theory/methodologies, such as adaptive control, as 

potential solutions to demanding flight control problems. Therefore, although motivated by a 

need within the SLS Program, the benefits extend to the NASA GN&C community. 

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5.0 Assessment Plan

The technical activities planned for this assessment occurred in two phases. The first phase 

consisted of the algorithm development, integration, and verification, while the second phase 

consisted of the research flights. The technical team initiated the first phase upon receiving 

authority to proceed, while completion of the second phase was contingent upon aircraft 

readiness. The two phases, as well as the key milestones for this assessment and the associated 

SLS Program design analysis cycles (DACs) and reviews, are shown in Figure 5.0-1. 

Figure 5.0-1. Key Milestones for Assessment and the SLS Program

The activities completed during each phase were as follows:

Phase 1: Algorithm Development, Integration, and Verification

The following algorithm development activities were accomplished at MSFC and Armstrong 

Flight Research Center (AFRC) with significant collaboration and integration:

• Modified the SLS FCS for F/A-18 FAST aircraft.

o Modified the interfaces and deactivated the DCA in baseline SLS FCS, then 

delivered for integration into the experiment software.

o Developed experiment-appropriate flight software using the ARTS IV research 

computer onboard the FAST aircraft, including switching logic to support 

multiple test cases (TCs).

• Developed SLS reference model and associated TCs and completed FAST NDI

modifications.

o Developed models using the SLS dynamics for use as a reference model in the 

FAST NDI controller. 

o Developed TCs using near- and off-nominal SLS scenarios.

o Developed an NDI controller that will achieve the SLS reference model 

dynamics, both nominal and off-nominal, within the capabilities of the aircraft.

• Defined flight plan/trajectory.

o Provided a trajectory guidance algorithm design and necessary parameter loads.

• Completed the following reviews:

o System Requirements Review at AFRC.

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o Technical interchange meeting at MSFC.

o Independent review team (IRT) joint Preliminary Design Review (PDR)/CDR at 

AFRC, requests for action (RFAs) from the IRT, and RFA resolutions from the 

documented IRT Airworthiness Review.

o Technical brief to the AFRC management to obtain concurrence of airworthiness 

and a signed flight request officially releasing the team to execute the flights as 

briefed.

• Documented and prepared flight package for review, including:

o Objectives and requirements document (ORD) (see Appendix G).

o Detailed hazard reports analyzing the potential asset and human risks associated 

with the experiment. Developed at a series of meetings of the System Safety 

Working Group.

o Software requirements and design document (SRDD) (see Appendix H).

o Verification and validation test plan (VVTP) (see Appendix I).

o Flight test plan (FTP) (see Appendix J).

• Completed verification and validation (V&V).

o Provided information and/or analysis as required to satisfactorily close IRT RFAs.

o Conducted V&V testing on the experiment software.

o Documented the flight media as tested and loaded on the aircraft in the 

configuration control process addressing any discrepancies from V&V.

• Completed flight preparation.

o Wrote flight test cards and completed piloted simulation evaluation of all planned 

test points.

o Developed and validated control room displays for real-time monitoring.

o Completed control room training via a real-time simulated mission.

o Executed a combined systems test to verify proper combined functionality of 

aircraft research and instrumentation systems, telemetry, and control room 

displays.

Phase 2: Flight Testing

Six research flights were completed using the SLS FCS. Multiple scenarios were flown, which 

map into each of the flight objectives described in Section 6.4 with emphasis on performing a 

functional check of the AAC. 

6.0 Background

In-house development of an AAC algorithm for launch vehicles as an addition to the classical 

control architecture began during the Constellation Program (CxP) [ref. 1]. Development of the 

adaptive algorithm was accelerated during the SLS Program, where the NASA community has 

continued to develop confidence in the algorithm through extensive internal and external 

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domain analysis of failure scenarios, and Monte Carlo analysis. Maturation of the algorithm 

coupled with the demonstration of tangible, predictable benefits led to the AAC algorithm being 

baselined as part of the autopilot design and flight software build prior to the SLS PDR in 2013.

The SLS Block I vehicle (see Figure 6.0-1) will deliver more payload to orbit and produce more 

thrust than any other foreign or domestic launch vehicle, opening the way to new frontiers of 

space exploration. Like all large launch vehicles, the SLS must balance the competing needs of 

maximizing performance while maintaining robustness. With its large size, high thrust, and 

multiple elements, the SLS is a highly flexible structure with complex bending characteristics. 

Control commands must be allocated to each of its six engines, which are actuated along the

pitch and yaw axes by thrust vector control (TVC) actuators with limited bandwidth. The 

massive propellant tanks have lightly damped lateral sloshing modes, and the uncertain payload 

envelope produces a unique set of parasitic dynamics. The SLS trajectories are optimized to 

maximize performance, which further challenges the flight control design and leaves little 

margin to share among multiple disciplines and subsystems.

Figure 6.0-1. SLS Block I Vehicle 

In the current fast-paced, low-cost development environment, there is a need for new 

technologies or design innovation to be coupled with affordable, quick-turnaround testing 

options. Testing the SLS FCS on a small-scale launch vehicle or a sounding rocket would 

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require the SLS software to be tailored to the existing vehicle specifications, including a reoptimization of the control parameters to provide the experimental platform with adequate 

closed-loop attitude tracking. Furthermore, these platforms lack key features of the SLS 

dynamics (e.g., low-frequency bending modes), which are needed to evaluate the adaptive law. 

Implementation on a piloted aircraft became an attractive alternative since AFRC demonstrated 

the F/A-18 FAST, shown in Figure 6.0-2, as capable of matching the dynamics of a reference 

model and safely accommodating a variety of control experiments. Furthermore, this testbed 

aircraft allowed for substantially longer flight times, multiple experiment conditions, and more 

data collection than the other platforms under consideration. Thus, the tests were conducted 

using the SLS FCS flight software prototype under SLS-based scenarios as though it was flying 

the SLS vehicle while hosted on the surrogate F/A-18 platform. Within a year of the concept 

study, a series of flight experiments was completed in November and December of 2013 on an 

F/A-18 aircraft, tail number (TN) 853. These flights were a key contributor to the maturation of 

the AAC technology, the only component of the SLS control architecture lacking flight heritage. 

The flights represent the completion of an important milestone toward the SLS baseline control 

design V&V.

Figure 6.0-2. F/A-18 FAST 

6.1 Motivation for Advanced Control 

The AAC scheme was designed to improve launch vehicle robustness and performance by 

adapting an otherwise well-tuned classical control algorithm to unexpected environments or 

variations in vehicle dynamics. The original motivation for considering the development and 

inclusion of an advanced control approach for human-rated launch vehicles did not result from 

an inability of the traditional fixed-gain control design to achieve satisfactory performance when 

the vehicle dynamics and environments were well-characterized. In the absence of vehicle or 

environmental uncertainty, a fixed-gain controller could be optimized such that there would be 

no motivation for on-line adaptation. However, a review of historical reusable launch vehicle 

data from 1990 to 2002 revealed that 41 percent of failures in other subsystems might have been 

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mitigated by advanced GN&C technologies [ref. 2]. Control approaches that use real-time 

adaptation thrive when there are environmental or vehicle model uncertainties, including inflight anomalies and failure scenarios. Traditional barriers to capitalizing on the benefits of 

advanced control techniques that are particularly relevant for human-rated systems include 

algorithm and code complexity, predictability of the response, ability to reconcile the stability 

analysis in the context of classical gain and phase margin, and flight certification. In other 

words, “black box” approaches with numerous adaptive gains, complex nonlinearities, and no 

way of estimating classical stability margins would be difficult to justify from a risk perspective, 

even if performance exceeded that of the existing architecture. Thus, an algorithmically simple, 

predictable AAC design compatible with classical stability margin assessment was implemented 

for the SLS.

6.2 Overview of the SLS AAC Algorithm

The philosophy that drove the formulation of the AAC algorithm, its initial testing during the 

CxP [ref. 1], and its refinement as part of the baseline autopilot design for the SLS [refs. 3 and 4] 

was to maintain nominal system performance and be compatible with classical stability criteria. 

Thus, an adaptive system was developed with a predictable response that augments the existing 

control architecture and prefers the nominal control gains. Secondly, the philosophy was to 

provide additional robustness using a simple architecture that could recover from poor closedloop performance and prevent or delay loss of mission (LOM) or loss of vehicle (LOV). To 

effectively accomplish this goal, model reference adaptive control logic was used within the 

adaptive update law to increase the control gain when excessive tracking error indicates that 

system performance is less than preflight predictions. An additional adaptive update term was 

included that decreases the adaptive gain when excessive power within a prescribed frequency 

band is observed in the control command as a result of control coupling with launch vehicle 

parasitic dynamics (e.g., flexibility, fuel slosh, and actuators). This gain-decreasing element is a 

feature that balances the competing objectives of reducing the tracking error while maintaining

stability of the plant dynamics not represented in the reference model. Only a subset of adaptive 

control approaches that appear in the open literature recognize the importance of decreasing 

performance to recover stability even if the error associated with the primary tracking objective 

is increasing. Figure 6.2-1 illustrates the as-needed gain adaptation design paradigm for the SLS 

AAC algorithm.

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Figure 6.2-1. The AAC Algorithm Design Paradigm is to Adapt on an As-needed Basis

Figure 6.2-2. Block Diagram of the SLS FCS with AAC

The resulting AAC algorithm was a forward loop gain (kT) multiplicative architecture that 

modifies the total attitude control system gain in response to sensed model errors (er) and 

undesirable resonance in the control loop (ys) resulting from control interactions with parasitic 

modes (see Figure 6.2-2). The AAC alters performance by increasing or decreasing forward 

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loop gain, thereby balancing attitude tracking with the attenuation of undesirable frequency 

content within the control path. In the case of the latter, this is often due to unmodeled or 

mismodeled parasitic dynamics that would otherwise result in a closed-loop instability or a 

potentially destructive limit cycle. In this case, the adaptive controller responds to the frequency 

content in the control signal by decreasing the total loop gain to reduce the interaction between 

these dynamics and the controller. The AAC algorithm is computationally simple and has 

stability properties that are reconcilable in the context of classical frequency-domain criteria. 

Since FCS guidelines provide a minimum of 6 decibels (dB) of rigid body margin and 6 dB of 

aero margin for the nominal vehicle, limits on the adaptive gain are restricted accordingly with a 

minimum value of 0.5 and a maximum value of 2. 

In the absence of vehicle or environmental uncertainty, a gain-scheduled controller can be 

optimized prior to flight, which would eliminate the motivation for adaptation. Adaptive control 

provides additional robustness by using sensed data to adjust the loop gain in flight. The 

algorithm was designed under the assumption that the gain-scheduled attitude control design is 

tuned for a nominal vehicle and trajectory at each flight time (i.e., adaptation should only occur 

should the vehicle performance lie outside the nominal design envelope), as depicted in Figure 

6.2-1. Therefore, by design the adaptive gain is attracted to unity through the use of a modified 

leakage term. The result is that only small alterations are made to the fixed-gain design within 

the expected dispersion envelope, without requiring the use of a dead zone in the update law. 

The AAC characteristics in simulation reflect the algorithm's three summary-level objectives:

1. “Do no harm;” return to baseline control design when not needed. 

2. Increase performance; respond to error in ability of vehicle to track commands. 

3. Regain stability; respond to undesirable control-structure interaction or other parasitic 

dynamics.

Note that the AAC was not designed to explicitly alter the ascent trajectory due to a lower 

performing vehicle (e.g., degraded thrust) or off-nominal environment (e.g., wind bias). Such 

techniques under the broader study of advanced guidance methods are beyond the scope of the 

AAC algorithm.

The AAC has been implemented in the SLS MAVERIC and SAVANT 6-degree-of-freedom 

simulation tools and was baselined for DAC-2. The SLS DAC-2 supplementary Monte Carlo 

analysis concluded no penalties associated with the algorithm and helped uncover other flight 

control performance artifacts (e.g., filter transition effects) [ref. 3]. These results and others 

completed via high-fidelity CxP Ares I and SLS simulations have demonstrated the capability of 

the AAC to “do no harm” in nominal scenarios, while increasing the attitude control robustness 

to vehicle and environmental uncertainties and failure scenarios. The result is a decreased

likelihood of LOM or an extension in the abort eligibility in the event of a catastrophic event

(e.g., case breach). A unique feature of the algorithm is its capability to protect against adverse 

control structure interaction.

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6.3 Motivation for Flight Testing

The AAC algorithm reached a high maturity level as part of the SLS autopilot design through 

simulation-based development, as well as internal and external reviews. However, leading into 

the SLS PDR, the AAC remained the only component of the vehicle control system that had not 

been flight tested. All other dynamic elements of the control system were successfully tested 

through Ares I-X [refs. 5 and 6]. Completion of the testing described in this paper means that 

every algorithm in the SLS control design architecture has been successfully demonstrated in 

flight, as shown in Figure 6.3-1.

Figure 6.3-1. Flight Testing of the SLS Autopilot Flight Software Prototype

6.3.1 TRL Assessment

One of the major objectives of this investigation was to raise the TRL of the AAC algorithm 

early in the SLS Program. The determination of an appropriate TRL maturity is not clear for all 

systems, leaving it somewhat to the discretion of the subject matter experts. However, 

identification of a system’s TRL provides consistent language for communicating the state of a 

technology across the Agency and should be considered with other factors. For SLS, the 

increased TRL gained through the research flights is part of a larger scale risk mitigation strategy 

that includes internal and external review, stability analysis, Monte Carlo simulations, and 

simulation-based SLS scenarios that exhibit anomalous behavior. 

NASA releases two categories under which the TRL of a system or subsystem can be assessed: 

hardware and software [ref. 7]. While the SLS FCS algorithms are coded into flight software, 

the software definitions point to a need to repeat a maturation process whenever there is a change 

in a software build or associated hardware. For the SLS flight software, the MSFC Software 

Integration Laboratory (SIL) will support the software maturation to TRL 8, but this does not 

imply that the algorithms will be fully vetted. Furthermore, when evaluating an algorithm’s 

readiness for use on a launch vehicle, it would not be logical to consider flight-tested methods 

(e.g., the standard architecture with a fixed-gain proportional-integral-derivative (PID) controller 

and bending filters) to have a low TRL. Taking these perspectives into consideration, the 

software definitions are mentioned herein, but the AAC algorithm TRL is defined based on the 

hardware description.

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Prior to testing the AAC algorithm through the F/A-18 research flights, the algorithm reached a 

TRL 5 level of maturity. The algorithm did not meet the TRL 6 criteria since the AAC had not 

yet been demonstrated in an operational environment. TRL 5 is summarized as “component 

and/or breadboard validation in a laboratory environment.” The associated hardware definition 

for TRL 5 is as follows [ref. 7]:

A medium fidelity system/component brassboard is built and operated to demonstrate 

overall performance in a simulated operational environment with realistic support 

elements that demonstrate overall performance in critical areas. Performance 

predictions are made for subsequent development phases.

The AAC algorithm was tested in a simulated operational environment. Performance had been 

demonstrated in critical functional areas through the use of a linear MATLAB®-based model 

(i.e., medium-fidelity system). Performance was evaluated based on the simulation results. 

The software definition associated with TRL 5 was met, defined as follows [ref. 7]:

End-to-end software elements implemented and interfaced with existing 

systems/simulations conforming to target environment. End-to-end software system 

tested in relevant environment, meeting predicted performance. Operational 

environment performance predicted. Prototype implementations developed.

End-to-end GN&C flight software elements have been implemented on multiple simulation 

environments, including a target computer by the flight software team. Algorithm performance 

is documented as part of the SLS GN&C performance assessment, which is released each design 

cycle. Agreement of test performance on the target computer is assessed based on the ability to 

meet specific metrics. 

The F/A-18 research flights increased the AAC’s TRL from 5 to 7, based on the hardware 

definition provided in reference 7. The descriptions and justification for reaching TRL 7, based 

on the hardware description and TRL 6 for both hardware and software descriptions, is provided 

in Figures 6.3-2 and 6.3-3, respectively.

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Figure 6.3-2. AAC TRL 7 Justification: After F/A-18 Flights

TRL 7 (Hardware Definition): A high 

fidelity engineering unit that adequately 

addresses all critical scaling issues is built 

and operated in a relevant environment to 

demonstrate performance in the actual 

operational environment and platform 

(ground, airborne, or space).

TRL 7 (Software Definition): Prototype 

software exists having all key functionality 

available for demonstration and test. Well 

integrated with operational hardware/

software systems demonstrating operational 

feasibility. Most software bugs removed. 

Limited documentation available.

An engineering unit is “built and tested so as to establish 

confidence that the design will function in the expected 

environments.” The F/A-18 was configured such that it was able 

to test out the full functionality of AAC. 

Also, “the relevant environment is the specific subset of the 

operational environment that is required to demonstrate critical ‘at 

risk’ aspects of the final product performance in an operational 

environment. It is an environment that focuses specifically on 

‘stressing’ the technology advance in question.” The pieces of the 

operational environment that were needed to demonstrate critical 

“at risk” of this technology were the use of a large-scale manned 

platform and attitude error matching capability for test cases that 

were specifically designed to stress the AAC. 

TRL 7: System prototype demonstration in an operational environment

Exit Criteria: Documented test 

performance demonstrating agreement 

with analytical predictions.

Exit Criteria: Documented test 

performance demonstrating agreement with 

analytical predictions.

NO

Prototype software not 

integrated into SLS’s 

operational hardware/ 

software systems. 

YES

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Figure 6.3-3. AAC TRL 6 Justification: After F/A-18 Flights

TRL 6 (Hardware Definition): A high 

fidelity system/component prototype that 

adequately addresses all critical scaling 

issues is built and operated in a relevant 

environment to demonstrate operations 

under critical Environmental conditions. 

TRL 6 (Software Definition): Prototype 

implementations of the software 

demonstrated on full-scale, realistic 

problems. Partially integrated with existing 

hardware/software systems. Limited 

documentation available. Engineering 

feasibility fully demonstrated. 

Meets TRL 7 requirements.

TRL 6: System/subsystem model or prototype demonstration in an 

operational environment

Exit Criteria: Documented test 

performance demonstrating agreement 

with analytical predictions.

Exit Criteria: Documented test 

performance demonstrating agreement with 

analytical predictions.

YES YES

Prototype implementation of the software – as 

compared with TRL 7 definition, this does not 

require full functionality. However, 

implementation was very similar to SLS DAC-3 

implementation.

The manned F/A-18 aircraft performing a gravity 

turn represents a full-scale, realistic problem. 

Software will be fully integrated with F/A-18’s 

existing hardware and software systems. 

Engineering feasibility will be fully demonstrated 

and results will be documented. 

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6.4 Objectives of the Research Flights

The F/A-18 FAST was used as a platform for conducting a flight characterization experiment 

intended to validate that the AAC algorithm achieves the purposes for which it was designed. 

Additionally, in consideration of the SLS requirement to incorporate manual steering capability 

that existed at the time this study was initiated, it is desirable to understand the pilot/ACC 

interaction. Thus, the primary objectives of the research flights are summarized as follows: 

1. Demonstrate closed-loop tracking with negligible adaptation in an environment that is 

commensurate with the nominal controller design envelope. 

2. Demonstrate improved performance in an environment where the nominal controller 

performance is less than desired. 

3. Demonstrate the ability to recover from unstable, mismodeled parasitic dynamics to a 

bounded nondestructive limit cycle. 

4. Explore interactions between manual steering and the AAC.

The approach for achieving these objectives was to develop multiple SLS scenarios that consider 

physically realizable normal and failure conditions that map into objectives 1 through 3. An 

additional TC was developed to demonstrate objective 3 by staging control-structure interaction 

with the physical resonance of the F/A-18 airframe. To achieve objective 4, a subset of these 

scenarios was repeated with the pilot providing attitude commands. 

Additional design considerations to enhance the value of the results were to maximize the 

dynamic similarity between the aircraft and SLS pitch attitude error dynamics, incorporate as 

much of the SLS FCS as possible to capture its interactions with the AAC, and force the AAC to 

respond to a physical realization of unfavorable SLS dynamics rather than cancelling highfrequency motion inside the aircraft's software package when possible. The experiment 

development process was structured to allow the SLS controls engineers to gain valuable insight 

toward the software integration and flight certification of the full-scale algorithm. In addition to 

validating the AAC in conjunction with the fixed-gain portion of the FCS, the process of 

developing TCs in the MSFC simulation environment, testing each of them in the F/A-18 

hardware-in-the-loop simulation (HILS), and then capturing flight test results allowed for 

verification that the flight software performed as expected.

7.0 Construction of Dynamically Relevant Research Flights

Combining the value of testing the AAC in a relevant environment with the need for an 

affordable testing option led to the unconventional approach of conducting simulated launch 

vehicle flight tests on a research aircraft. Control experiments have been supported previously 

on AFRC’s F/A-18, but this is the first time the aircraft was considered for testing an algorithm 

specifically designed and tuned for launch vehicles. To aid in understanding the relevance of the 

test results, this section provides an overview of the aircraft platform and key features of the 

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implementation for the launch vehicle flight experiment. The following sections do not capture 

every detail of the implementation but are intended to illustrate at a basic level the relevant 

aspects of the test platform and implementation for the launch vehicle experiment.

7.1 Applicability of the F/A-18 as an SLS Flight Test Platform

This flight characterization experiment was performed on the F/A-18 (TN 853) FAST platform 

[ref. 8]. This aircraft is maintained by AFRC and was developed specifically to accommodate a 

variety of controls experiments with full command authority of the F/A-18 and its available 

control surfaces. Aided by the presence of a qualified research pilot, the F/A-18 TN 853 has an 

experimental envelope called the Class B envelope that is defined by altitude and Mach 

constraints (Figure 7.1-1). Constraining experiments within this envelope ensures safe recovery 

from unusual attitudes and configurations. The favorable recoverability characteristics and the 

structural robustness of the aircraft while inside the Class B envelope allowed AFRC engineers 

to pre-clear the aircraft for full-authority experimental control while inside the prescribed altitude 

and Mach constraints. The aircraft features production and research sensor inputs, pilot 

experiment engage/disengage capability, real-time configuration of multiple experiments on a 

single flight, as well as failure detection with automatic reversion to the fail-safe production 

mode. As with the traditional FCS, the experimental controller inside the Class B envelope is 

able to control ten aerodynamic effectors and two throttles. 

Figure 7.1-1. Class B Envelope and Sample Trajectory

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The idea that an F/A-18 can represent a relevant environment for the purpose of advancing the 

TRL of an adaptive control technique for the SLS vehicle is novel and requires an in-depth 

explanation. The FAST F/A-18 was not the only candidate considered for testing. The other 

platform options will not be discussed in this section, as the intent is to explain the key features 

of the FAST vehicle that make it a relevant test platform.

The first key to performing a meaningful test on the F/A-18 is that the aircraft has sufficient 

performance, even when restricted to the class B research envelope, to fly a near-ballistic 

trajectory, which is a reasonable approximation of the SLS gravity turn boost trajectory. The 

shape of the pitch rate command for the F/A-18 zoom climb and subsequent near-ballistic 

trajectory was designed to be similar to that of the SLS to solid rocket booster (SRB) separation. 

More details of the trajectory can be found in Section 7.2. An illustration of the trajectory 

projected onto the Class B envelope restrictions (i.e., altitude and Mach ranges) is shown in 

Figure 7.1-1.

In addition to the command shape similarity provided by the zoom climb trajectory, another 

important aspect of dynamic similarity is matching the rigid body attitude dynamics along that 

trajectory. Matching the SLS attitude dynamics was accomplished by the F/A-18 inner loop NDI

controller, which forced the aircraft to exhibit the angular acceleration response of a given plant 

model. As a result, the responses of the F/A-18 pitch angle error, pitch rate, and pitch 

acceleration were nearly the same as predicted for the SLS vehicle and could be used as inputs to 

its FCS. In this way, the SLS FCS “thinks it is flying SLS” when it is engaged on the nearballistic F/A-18 trajectory. In addition to matching the SLS rigid body pitch dynamics, the NDI 

provided the FAST vehicle with the ability to track other aspects of the SLS (e.g., simulated 

atmospheric disturbances), parasitic dynamics (e.g., structural modes), and various failure 

modes. This inner loop controller is described in greater detail in Section 7.3 and in Appendix F.

The SLS performance, combined with the dynamic similarity, resulted in a test trajectory of 

approximately 70 seconds. This represents a sufficiently large portion of the SLS boost 

trajectory, such that the time scaling of the adaptive controller during the F/A-18 tests can 

directly match the time scaling for the SLS vehicle. This feature allows for verification of the 

adaptation rates, gains, and architecture, requiring minimal changes to the flight configuration 

SLS controller.

The flexibility of the research flight control computers on the FAST vehicle allowed

accommodation of software from various sources and programing languages. These flight 

computers, combined with the SLS-like trajectories, suitable flight times, and the NDI, allowed 

for hosting of the flight prototype C and C++ SLS control laws on the F/A-18. This capability 

allowed the team to test for software errors using AFRC’s F/A-18 HILS and during the research 

flights. As a result, software findings during the preparation or execution of the LVAC research 

flights were corrected in the SLS prototype flight software.

Additionally, the FAST platform, with the ability to remove the production F/A-18 structural 

filters and excite the existing structural modes, provided a test scenario for the adaptive 

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controller to respond to a physical, rather than a modeled, airframe structural mode. While the 

F/A-18 first fuselage mode was at higher frequency than the lowest frequency SLS structural 

modes that typically drive the control design, the aircraft fuselage mode was in the range 

typically analyzed by the SLS Controls Working Group during each SLS design cycle. Due to 

the structural robustness and limited dynamic pressure (q) within the Class B envelope, preflight 

analysis showed that the aircraft mode could be excited through the use of a programmed test 

input (PTI) and an intentionally destabilizing FCS design with negligible risk of aircraft damage. 

This provided the LVAC experiment with an ideal test scenario for evaluating the AAC response 

to a structural mode in a safe but relevant way. The F/A-18 structural mode was more highly 

damped than the expected SLS modes in the same frequency range, which has implications on 

the frequency of the resulting stable limit cycles when the AAC was activated but does not 

negate the value of the test involving this mode.

The presence of a test pilot and the ability to drive pilot cueing displays allowed for the testing of 

the interactions between a manual steering mode and the adaptive controller. This test objective 

was of lower priority, but provided an environment to explore manual steering implementation. 

The test uncovered important deficiencies that will need to be addressed if manual steering is 

required, and if manual steering is used in combination with the AAC on the SLS. It is important 

to note that the manual steering mode evaluated during this experiment was not the official SLS 

manual steering mode design or implementation, as an official design did not yet exist.

Finally, the nature of the FAST architecture allowed for an array of test scenarios during a single 

mission and allowed the researchers to reconfigure the experiment between flights with minimal 

recertification testing. This rapid prototyping capability, combined with the ability to repeatedly 

test numerous SLS-based scenarios in rapid succession, made the platform not only relevant with 

respect to the stated flight test objectives, but attractive from a cost and schedule perspective. 

Other options (e.g., sounding rockets) were explored, but testing the first three AAC objectives 

would not have been possible due to the limited number and configurability of the tests within 

the same budget constraints. Furthermore, this option would not provide the test times and 

repeatability offered by the FAST platform. While other NASA test assets (e.g., AirSTAR)

could provide similar computational and software integration capabilities, the tests would have 

been performed at faster time scales, which may have required extensive scaling computations

and analysis to justify the utility of the test results. The FAST platform offered more test and 

longer maneuver times than a single rocket or missile test, thereby providing increased

opportunity to fully and repeatedly exercise many aspects of the algorithm. Finally, the use of a 

manned vehicle required that the experiment development process be reconciled with appropriate 

safety and risk mitigation requirements, thus enhancing the applicability of both the tested 

algorithm/software package and the experience of the development team on the operational 

platform.

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7.1.1 Description of the F/A-18 FAST Platform

FAST is a single-seat F/A-18 aircraft, as shown in Figures 7.1-2 and 7.1-3. Substantial research 

instrumentation (e.g., structural, air data, and inertial) has been installed to support a wide range 

of research activities. The robust nature of the testbed (i.e., structural load capacity, spin and 

recovery characteristics, and reversion to production control laws), with the research 

instrumentation, enabled flight testing of novel control laws on a piloted flight vehicle with 

minimal validation testing requirements.

Figure 7.1-2. F/A-18 in Flight

Figure 7.1-3. F/A-18 Profile

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Figure 7.1-4 shows the FAST control computer and system architecture. The system maintains 

the advantages of the production system and utilizes its redundancy management architecture for 

sensor selection and actuator signal management. The FAST research flight control computer 

architecture, which builds upon legacy F/A-18 research systems, consists of two separate 

research processing capabilities. The research flight control system (RFCS) provides a minimal 

delay quad-redundant processing environment in which Ada-programmed experiments can be 

executed. The RFCS performs some protection for restricting the engagement envelope of a 

given research control law hosted in either the ARTS or the RFCS (see Figure 7.1-1). The 

ARTS, with its more capable processor and the ability to host Simulink® autocode or C code,

provides a more flexible environment for novel control laws than the RFCS. However, the 

ARTS is only dual-redundant and imparts one additional frame of delay to commands. This 

extra frame of delay translated to 0.0125 seconds for the pitch and roll axes and 0.025 seconds

for the yaw axis.

The LVAC experiment took advantage of the ample computation and data storage capabilities in 

the ARTS, as well as the autocoding capability supported via a MATLAB®/Simulink® modelbased design environment. The flight software prototype of the SLS autopilot, already coded in 

C, was also hosted within the ARTS. 

Figure 7.1-4. RFCS/ARTS

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Additionally, the FAST research system was designed to provide the experimenter with the 

flexibility to configure the experiment in flight through the use of selectable parameters that are 

entered by the pilot. As configured for LVAC, the FAST aircraft was able to support up to 

221 unique TC scenarios. Any of these scenarios that have been configured and appropriately 

verified prior to flight could be engaged as the active experiment configuration during a flight 

test mission. This provides the researcher with a great deal of flexibility when designing and 

testing cutting-edge control technologies, and facilitates highly effective research missions. For 

the LVAC experiment, this array of configurable options was essential due to the limited number 

of sorties and the flight schedule, which was compounded by the large number of TCs desired to 

verify the ACC design objectives.

The ability to provide command cueing to the pilot from an ARTS experiment is a new 

capability that was added to the FAST vehicle specifically for the LVAC manual steering 

objective. Experimenters now have the ability to provide a visual command reference to the 

pilot using the production instrument landing system (ILS) display on the FAST vehicle from a 

research experiment hosted on the ARTS. This feature allows researchers to design maneuvers 

for the human pilot and quantitatively explore how well the pilot is able to track the trajectories 

for a wide range of control configurations and simulated failure scenarios. This is a valuable 

capability for experimenters exploring the interaction of a pilot with the control technique.

Lastly, the FAST system includes communication with the aircraft research instrumentation 

system. This capability allows for utilization of many research instrumentation measurements as 

closed-loop control law feedback sensors. This feature expands the types of control architectures 

and novel sensor experiments that can be facilitated by the platform. For the LVAC experiment, 

this capability was utilized to explore how the AAC algorithm performed with two different 

pitch rate sensors installed at separate locations on the aircraft to amplify the physical F/A-18 

airframe structural response in the control path.

7.2 Experiment Trajectory

The aircraft's trajectory was designed to track a pitch rate of approximately 0.75 degree/second, 

which was used as an input to the SLS FCS during the experiment in lieu of SLS’ outer-loop 

guidance commands. This rate is near the average pitch rate of the SLS during its gravity turn 

prior to SRB separation. During project formulation, an exact zero-g (gravity) turn was 

considered as a candidate test trajectory. However, the FAST aircraft is prohibited from 

sustained zero- and negative-g maneuvers. Additionally, attaining these low-g trajectories 

required high pitch rates and would have resulted in much shorter test trajectories. The 

experiment was not designed to match the aircraft's pitch attitude to that of the SLS, since this 

would require a near-vertical flight path. That profile is not achievable since the aircraft’s thrustto-weight ratio is less than unity at the altitudes at which experiment is flown. The need to 

perform the experiment inside the Class B envelope further constrained the aircraft trajectory 

design since initiating the trajectory at a high pitch attitude would result in the aircraft rapidly 

losing speed and flying out of the envelope, thereby reducing the time available on each 

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trajectory to perform the controls experiment. Thus, the trajectory was designed to provide pitch 

rates that closely approximate those of the SLS and maximize the experiment time, given the 

constraints imposed by the Class B envelope. Figure 7.1-1 shows a sample trajectory (blue/green

lines) that achieves the desired test time (green line) within the Class B experimental envelope 

(black lines) during a simulated flight.

A diagram of the resulting trajectory design is shown in Figure 7.2-1, starting with the setup 

maneuver (blue line) leading to the operation of the SLS FCS (green line). Prior to initiating 

each test scenario, the pilot accelerated the aircraft to approximately 330 knots (Mach 0.5) and 

19,000 ft, and then rotated to 35-degree pitch attitude. This resulted in a decreasing Mach 

number as the aircraft pitched up and entered the Class B envelope, which needed to occur at a 

low enough altitude that aircraft did not exceed the maximum altitude constraint at the peak of its 

trajectory (see Figure 7.1-1). Once at the desired altitude, Mach, and pitch attitude, the pilot 

armed and engaged the desired SLS experiment. The entrance criteria for the experiment were 

not exact and had a small impact on the control surface effectiveness and available trajectory 

time. Thus, the overall repeatability of the experiment results was not significantly impacted by 

the variability of the maneuver initiation. Furthermore, the pilots were able to achieve the 

insertion conditions with the precision needed to complete the trajectory within the Mach and 

altitude constraints prescribed by the Class B envelope for a majority of the attempts (see 

Appendix A for documentation of complete/incomplete trajectories). After the experiment was 

engaged, the autopilot tracked the desired pitch rate unless manual steering was engaged. The 

pilot monitored the experiment and managed aircraft throttle near peak altitude to extend the test 

time within the Class B velocity envelope as it began its pitch downward. Engine gyroscopic 

coupling and the aerodynamic disruption of the sudden change in engine inlet flow resulted in a 

pitch transient due to the de-throttle, but the pilots were able to ensure a smooth, gradual throttle 

transition, which minimized this undesirable pitch transient and the subsequent effect on the test 

results.

Figure 7.2-1. Prescribed Trajectory for the Flight Experiment

During a typical sortie, between 16 and 29 trajectories approximately matching the one described 

in Figure 7.2-1 were completed within the available airspace until fuel constraints required the 

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aircraft to land. Each time the trajectory was repeated during flight, the aircraft was configured 

with a different SLS test scenario. Images taken of FAST performing this trajectory as part of 

the SLS experiment are shown in Figure 7.2-2. Exceptions to the aforementioned trajectory 

implementation were for (1) manual steering, where the pilot sought to track the desired attitude 

rate while performing the experiment, and (2) experiments to detect, excite, and mitigate an 

airframe structural mode, during which the pilot maintained straight and level flight.

Figure 7.2-2. Photographs Depicting the Trajectory Flown Repeatedly by the F/A-18

7.3 Nonlinear Dynamic Inversion to Achieve Pitch Attitude Matching

A key to the F/A-18 representing a relevant test environment for the AAC technique as it is 

applied to the SLS autopilot was that the relevant measured dynamics (e.g., pitch error and rate) 

and control responses of the FAST vehicle had to be made to match those of the SLS vehicle 

during the boost phase prior to SRB separation. The justification for advancing the ACC TRL in 

a relevant environment was dependent on the pitch attitude dynamic response matching across a 

wide range of desired frequencies to the bandwidth limitations of the F/A-18 rigid body and 

actuator responses.

The NDI baseline control law was developed as part of the FAST suite of research capabilities

[refs. 9 and 10]. It has been used on a number of experiments to provide desirable vehicle 

control response characteristics for the control technique under test. It is based on a simple 

conservation of angular moment formulation. Given a three-axis desired angular acceleration,

the NDI computes the necessary F/A-18 control surface commands required to generate the 

commanded angular acceleration. The NDI utilizes the F/A-18 aerodynamics with nonlinear 

rigid body equations of motion to divide out the underlying airframe dynamics and replace them 

with those of the desired plant.

For the LVAC experiment, the desired plant was that of the SLS vehicle. A planar, high-fidelity 

SLS simulation using linearized plant dynamics and nonlinear actuator models [ref. 11] was 

delivered and implemented as the desired dynamics for the FAST vehicle to track. This model

(i.e., reference model) runs in the ARTS with the SLS control laws and provides the NDI with 

reference commands to track. The model contains the relevant dynamics (e.g., structural modes, 

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slosh modes, actuator dynamics, rigid body characteristics, failure modes, environmental 

disturbances, etc.).

One unique aspect of the LVAC experiment was the requirement to generate different aspects of 

the vehicle dynamics with the F/A-18 control surfaces. This requirement was driven by the test 

scenarios involving the SLS vehicle elastic response. For the tests involving structural modes to 

be of maximum value, a different set of control surfaces is used to generate the simulated SLS 

structural dynamics on the F/A-18 rigid body from the set of surfaces used to respond to those 

dynamics. If the same set of surfaces is used for both tasks, then the experiment is effectively 

generating and canceling out the structural dynamics inside the software, which provides 

significantly less benefit with regard to flight over a ground simulation. Generating the 

dynamics with one set of surfaces and then forcing another set to respond to the dynamics 

generated effectively closes the loop outside the software around the aircraft, significantly 

increasing the value of the TCs. The NDI was modified from the form used by previous 

experiments such that the ailerons were used in a symmetrical manner to generate the elastic 

contribution to the pitch axis dynamics, and the stabilators were used to respond to those 

dynamics through the simulated SLS vehicle rigid body. The importance of maximizing the

value of these simulated structural mode TCs was heightened by the team’s uncertainty about 

the feasibility of the structural TC utilizing the F/A-18 first fuselage bending mode. Ultimately, 

both TCs functioned as desired, providing firm confidence in the ACC algorithm TRL.

LVAC test scenarios flown on the F/A-18 were designed using the linear high-fidelity SLS 

simulation [ref. 11] employing model parameters consistent with the DAC-2. As part of that 

LVAC simulation, a second-order, time-varying aircraft model was provided by AFRC to 

account for the NDI and the F/A-18 tracking performance throughout the LVAC trajectory. 

The resulting scenarios and the SLS model, including plant and controller, were supplied to 

AFRC, where they were verified in the HILS as part of a formal V&V process. The HILS results 

matched with the flight data and the MSFC simulation results. 

7.4 SLS Reference Dynamics

The LVAC dynamic inversion function reference model was based upon a quasi-linear 

perturbation flight mechanics formulation derived from a planar, time-varying version of the 

Frequency Response Analysis and Comparison Tool Assuming Linearity (FRACTAL) model. 

This model provided the reference states that were duplicated by the F/A-18 pitch response so as 

to simulate launch vehicle dynamics along the experiment trajectory. FRACTAL is a tool for 

flight control design and analysis and was derived from the standard modal superposition 

approach to flexible launch vehicle dynamics [refs. 11 and 12]. This model, within limitations of 

the planar nature of the simulated flight dynamics, was consistent with the model approach used 

for the SLS vehicle flight control design. 

The LVAC reference dynamics produced a set of reference states as a function of time since 

trajectory initiation, which was used by the FAST dynamic inversion controller. In general, the 

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rate of change of the reference states is considerably less than the angular acceleration and rate 

capability of the F/A-18 airframe pitch dynamics. This enabled accurate tracking of the 

reference model with negligible model error.

During the reference model formulation development, performance limitations in the execution 

of a simulated launch vehicle ascent profile by an aircraft precluded duplication of a true gravityturn trajectory. Furthermore, the FAST aircraft trajectory could not duplicate the launch vehicle 

angle of attack (α) profile through the use of short-period attitude dynamics. This is a key 

trajectory parameter affecting launch vehicle flight control, which is dependent on air-mass 

relative velocity. Thus, the LVAC reference model was formulated in such a way that α, crucial 

for simulating aerodynamically unstable launch vehicle pitch dynamics, was computed using a 

hybrid scheme. In this manner, a small angle linearization dependent solely on the reference 

states was replaced by a computation that is dependent on a true kinematic state of the aircraft 

(i.e., the pitch error) and an internally propagated lateral velocity that simulates the vehicle error 

velocity with respect to the trajectory. Under the assumption that the reference trajectory is a 

zero-α trajectory (as is the case for SLS boost-phase flight), the aircraft trajectory-relative pitch 

error and the internal velocity estimate were combined to compute the internal α.

The LVAC reference formulation included all relevant dynamics associated with the pitch axis 

of the SLS launch vehicle, including the time-varying rigid-body mass properties, core stage 

oxidizer and fuel sloshing modes, six bending modes,1 and nonlinear actuator dynamics 

associated with each of the six engines (including nozzle inertial coupling effects). Timevarying structural dynamics were implemented using the quadratic inequality constrained least 

squares (LSQI) transition method [refs. 13 and 14]. The top-level organization of the reference 

model is shown in Figure 7.4-1.

1 Bending modes are sorted by frequency after selecting dominant modes for the pitch axis. Additional modes are 

supported in the model but were truncated to six based on frequency, due to limitations in the F/A-18 surface 

actuator rates. The maximum supported bending frequency is approximately 3.4 Hz.

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Figure 7.4-1. LVAC Launch Vehicle Reference Model Architecture

Because the LVAC dynamic simulation relied on a single rate reference (e.g., a single inertial 

measurement unit (IMU)) and the airframe rigid body dynamics were used to simulate the launch 

vehicle elastic response, direct simulation of multiple-sensor blending was not feasible. 

However, SLS employs a composite blended sensor output from multiple rate sources, including 

the forward-mounted redundant inertial navigation unit and the intertank and core aft skirt rate 

gyro assemblies (RGAs). To support accurate modeling of the sensor blending effects, the 

simulated elastic rate measurements were combined inside the model prior to generating the 

reference angular rate output for the FAST inversion controller.

Based on time-and-frequency domain analysis of the aircraft nonlinear dynamic inversion 

function, it was determined that the low-pass and delay characteristics of the airframe tracking 

dynamics were similar to those of the true SLS sensor dynamics and transport delay. Since SLS 

sensor nonlinearities are well characterized and have negligible effect on the boost-phase attitude 

dynamics, the aircraft sensors (having performance less than that of a launch-vehicle grade 

navigation instrument) could conservatively capture the dominant effects. Therefore, the SLS 

sensor models were removed, and the airframe served as an effective surrogate for the SLS 

sensor and avionics chain.

Internal integration of the continuous-time core vehicle dynamics was performed at the FAST 

frame rate of 160 hertz (Hz) using a custom Runge-Kutta fourth-order integration routine with 

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support for the LSQI transition logic. The linear portion of the actuator dynamics, where 

parameters are constant over the flight regime being studied, was explicitly discretized.

Environmental effects and failure injection was implemented in support of LVAC test scenarios. 

In addition to direct modification of plant parameters (e.g., slosh damping), these features 

include random (band-limited) turbulence and prescribed wind gust inputs, engine out, actuator 

hardover and fail-in-place events, and prescribed torque disturbances.

Band-limited turbulence was implemented in terms of a trajectory-relative velocity profile and 

was generated using a filtered zero-mean Gaussian random profile having a 1– σ

2

α perturbation 

of 3 degrees with a bandwidth of 0.2 Hz. This profile provided vehicle dynamic excitation, 

especially sloshing and bending modes, commensurate with the launch vehicle flight 

environment in the case that the true (flight-day) natural environment was unusually quiescent. 

For certain TCs, a wind shear was superimposed on the baseline turbulence profile. This shear 

profile was derived from a 3-σ Global Reference Atmosphere Model (GRAM) Monte Carlo run 

used for loads case verification during the CxP Ares I-X vehicle development. This shear was 

particularly stressing as it accumulates significant bias during the early portion of flight and 

undergoes a large change in magnitude in the region of maximum q. An example shear profile, 

including the baseline turbulence profile, is shown in Figure 7.4-2. Note that the profile sign was 

reversed in shear cases so as to induce a pitch-up disturbance to the F/A-18 airframe near the end 

of the trajectory. This prevented excessive nose-down attitude of the aircraft just prior to 

experiment termination.

2 Internal to the reference dynamics, the angle of attack is computed using a small-angle approximation as a function 

of the trajectory normal velocity and the simulated (launch vehicle) Earth-relative velocity V(t) [ref. 11]. The 

variance of the velocity profile is scaled by the Earth-relative velocity such that the variance of the resultant angle of 

attack perturbation is constant (see Figure 7.4-2).

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Figure 7.4-2. Reference Model Wind Profiles: Baseline (left) and Shear (right)

To support test operations, outputs of the reference model were used to compute certain 

termination conditions that were implemented for FAST automated disengage during each 

trajectory. These conditions indicated the occurrence of a failure mode that was unrecoverable 

and called for experiment termination. The primary termination conditions indicate a simulated 

structural load exceedance based on excessive bending strain energy or the product of rigid-body 

q-α. For the purposes of the LVAC experiment, structural failure was assumed to occur if the 

simulated vehicle tip deflection exceeded 5 ft or the rigid-body load indicator exceeded 

8,500 degree-lbf/ft

2

.

The general approach to the derivation of the FRACTAL dynamics for this model was 

extensively documented in references 15 and 16.

8.0 Flight Control Implementation and Test Scenarios

This section provides an overview of the SLS FCS and its implementation for the flight 

campaigns. The SLS scenarios that were developed and flown are described herein.

8.1 SLS FCS Overview

The SLS FCS (Figure 8.1-1) is based upon a PID algorithm supplemented by disturbance 

compensation elements for translation and rotation. The primary mechanism is feedback control 

of the vehicle attitude dynamics via sensing of attitude errors and vehicle body rates, while 

simultaneously rejecting parasitic elastic and propellant sloshing dynamic response. In order to 

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effect a load relief and anti-drift function, small adjustments to vehicle attitude are used to 

control the vehicle translational state with respect to a trajectory-referenced equilibrium.

Figure 8.1-1. SLS FCS Overview

Vehicle commands are issued in terms of angular acceleration at a frame rate of 50 Hz (80 Hz in 

LVAC implementation with appropriate scaling of filter coefficients). The PID controller is 

preceded by optimized linear bending filters, which provide appropriate phase shaping and gain 

attenuation such that vehicle flexibility and fuel sloshing dynamics remain adequately attenuated. 

Filters are implemented in a series fourth-order transfer function mechanization to avoid 

numerical difficulties encountered with high-order filters (e.g., coefficient truncation and state 

quantization). 

Angular and translational DCAs borrow from the form of the in-flight load-relief/anti-drift 

algorithms used in the Ares I and Ares I-X autopilots. The DCA elements operate on the 

principle of constructing estimates of unmeasured dynamical variables (e.g., angular 

acceleration). These estimates are used to deduce the magnitude of external disturbances, which 

are compensated with an appropriate lateral or angular acceleration command. Since the 

disturbance compensation algorithms rely on vehicle lateral acceleration measurements that 

cannot be reproduced by the FAST platform, the DCA algorithms were not exercised in flight 

test.

The SLS vehicle relies on pitch/yaw rate data from multiple rate sensors, including two RGAs

and the primary IMU located in the forward instrument ring. The three rate measurements are 

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blended using scheduled optimal weights to maximize structural mode attenuation prior to 

filtering.

Since SLS’s multiple-engine configuration yields redundant control authority in three axes, a 

gain-scheduled linear control allocation scheme is used to optimally allocate commands to the 

engine actuators to achieve the commanded angular acceleration, while minimizing a weighted 

norm of the control deflection. Weight computations are performed on-line in the flight software 

using tabulated a priori data detailing the engine locations, vehicle mass properties, and thrust 

profiles. Online computation allows the algorithm to reconfigure and accommodate a loss of 

engine thrust, guidance throttling, and the end of the SRB burn with a negligible change in the 

total loop gain.

Flight certification of the SLS FCS is expected to follow from a classical procedure involving 

trajectory-relative linearization of the vehicle dynamics and autopilot open loop (annotated in 

Figure 8.1-1) at discrete operating points supplemented by nonlinear time-domain Monte Carlo 

simulation and statistical analysis. The Nyquist criterion is applied to the open-loop frequency 

response, and the linear, time-invariant stability margins are assessed. In addition, the SLS 

Program has employed a scalar technical performance metric (TPM) based on the Nichols disc 

margin, analogous to the “point of closest approach” technique used in the Evolved Expendable 

Launch Vehicle community. The disc margin is calculated in the Nichols plane by computing 

the scale factor of the largest disc that can be inscribed in the interior of the frequency response 

near the midpoint of the low-frequency (aerodynamic) and high-frequency (rigid-body) crossings 

of –180 degrees of phase. The disc scale factor is compared with that of a reference disc that has 

dimensions based on industry-standard values of gain and phase margin. The ratio between the 

actual disc and reference disc is used to generate a scalar metric of flight control design margin.

Since the disc centroid is allowed to vary independently of the forward loop gain, the analysis 

process allows the program to allocate classical stability margins to the AAC algorithm without 

affecting the value of the TPM.

A comprehensive overview of the SLS FCS design can be found in reference 17.

8.2 Flight Software Prototype Implementation

To integrate the SLS FCS with the SLS reference model and F/A-18 NDI controller such that the 

algorithms could be autocoded onto the ARTS software platform, a Simulink® S-function of the 

SLS FCS was developed and interfaced to the Simulink® SLS reference model. The S-function 

passes and receives signals to and from the C/C++ SLS flight software prototype, the same code 

executed in the SLS GN&C development simulations and in the SIL. The FCS design that was 

employed, including the digital filter parameters and gains optimized for SLS, matches those 

used for DAC-2, although the AAC component was updated from the DAC-2 to the DAC-3 

implementation as described by Wall et al. [ref. 3]. Originally, the entire experiment was 

designed using the DAC-2 SLS models and FCS. However, flight delays afforded the team the 

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opportunity to integrate AAC algorithm updates that were incorporated into the DAC-3 version 

of the FCS. The remainder of the FCS and models were based on the DAC-2 versions. 

There were several changes made to the flight software prototype to facilitate implementation of 

the flight experiment. As multiple flight tests were to be flown for a single executable version of 

the flight software and specific tests contained flight control parameter modifications, 

adjustments to the original initialization and parameter definition logic were made. In the SLS 

flight software implementation, a single parameter initialization function suffices for a single 

flight. For the LVAC experiment, logic was employed to allow for selection amongst a 

precompiled set of initialization functions, each having unique parameter sets. Additional 

modifications to the initialization functions were made to ensure the internal variables containing 

history (e.g., integrators, filter states) were reset between executions of subsequent TCs. Since 

the FCS architecture has the same form across all axes, for simplicity the control experiment for 

LVAC was implemented on the pitch-axis only. In the operational SLS vehicle, the control 

allocation framework accounts for the SRB TVC system being rotated 45 degrees about the 

booster roll axis (TVC actuation is split between the pitch and yaw axes). Since this is a pitchonly experiment, the control allocator within the FCS, as well as the SLS reference model, 

assumed the TVC system to be in-plane with the pitch axis. The controller was modified to 

schedule gains and parameters with respect to SLS trajectory time rather than other navigated 

independent variables used in the production system. The SLS FCS includes attitude rate sensor 

blending, but the effects of sensor blending were moved into the dynamics model since the 

aircraft uses a single sensor for attitude and rate. In contrast, the SLS requires a blend of 

multiple sensor measurements to achieve attenuation of structural modes. The autopilot's 

discrete filters were reoptimized for the same phase and attenuation constraints as the original 

50-Hz filters, but with a modified rate at 80 Hz to accommodate the update rate of the F/A-18 

pitch axis control system. An example rate error filter comparison for the max-q portion of the 

SLS ascent flight is shown in Figure 8.2-1. The filter response only needed to match in the basic 

shape and attenuation to provide ample stability in the nominal cases and allow for intentional 

destabilization of flex modes in off-nominal cases, while providing traceability to the SLS 

DAC-2 configuration design requirements. The only part of the SLS autopilot architecture 

disabled for the LVAC experiment was the DCA since the spacecraft translational dynamics 

could not be replicated by the aircraft.

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Figure 8.2-1. Comparison of Pitch Rate Filter after Reoptimization at Target Update Rate

8.3 Description of SLS Scenarios Implemented During Flight

Several scenarios were designed based on the SLS DAC-3 models. Vehicle, environmental, 

and/or control parameters were modified to simulate TCs based on extreme, but physically 

realizable scenarios that map into the flight test objectives. These TCs are summarized in 

Table 8.3-1, and complete descriptions are provided in Appendix B. 

Table 8.3-1. Full Set of TCs Generated for Research Flights

TC DESCRIPTION OBJ AAC FT 1 FT 2 FT 3 FT 4 FT 5

0 Nominal plant, environment, and

controller 1 OFF A A M(2) M(2)

ON A(3) A(2) M(2) M(2)

1 Heavy/slow vehicle 1 OFF A

ON A

2 Light/fast vehicle 1 OFF A

ON A

3 Wind shear event 2 OFF A

ON A

4 TVC bias 2 OFF A(2)

ON A

5 Hardover failure of two core engines 

(offset in time) 2 OFF A M M

ON A M M

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6 HEAVY/SLOW, WIND SHEAR, SRB 

TAILOFF THRUST IMBALANCE 2 OFF A

ON A

7 Wind shear event and double hardover

failure 2 OFF A M M

ON A M(2) M

8 Case Breach 2

9 Light/fast with slosh instability 3 OFF A A

ON A A

10 Structural instability 3 OFF A(2) A

ON A A

11 Thread-the-needle I 2&3

12 Thread-the-needle II 2&3

13 Low-gain controller with wind shear 

event 2

14 Low-gain controller, wind shear, 2 

hardover failures 2 OFF A

ON A

15 High-gain controller, slosh instability 3 OFF A A MA(2)

ON A A MA

16 High-gain controller, unstable flex 3 OFF A A MA(2)

ON A(2) A MA

17 High-gain controller, rigid body 

instability 3 OFF A A MA(2) MA(2)

ON A A(2) MA(2) MA(2)

18 High-gain controller, structural and

slosh instabilities 3

19 Identification of F/A-18 structural 

mode 3 OFF S/L(3) 

20 F/A-18 structural mode amplification 

with inertial navigation system (INS) 3 OFF S/L S/L(2)

ON S/L S/L

21 Ground test mode This mode used for preflight checks

22

F/A-18 structural mode amplification 

with embedded global positioning 

system (GPS) INS (EGI)

3

OFF S/L S/L(4)

ON S/L S/L(2)

“A” – Experiment conducted in autopilot mode; “M” – manual mode (pilot steering commands); 

“S/L” – straight and level flight; number in parentheses indicates number of times TC was flown if 

greater than 1; gray rows represent TCs that were designed but did not fly.

Each of these TCs was simulated at MSFC with a reduced-order time-varying F/A-18 model in 

the loop with the AAC both off and on. Each case was subsequently tested with the AAC off 

and on in the F/A-18 HILS at AFRC. Finally, a subset of the TCs was flown on the FAST 

aircraft. 

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9.0 Summary of Research Flights and Results

Two series of flights have been conducted using the SLS autopilot prototype software on the 

FAST platform. The first series consisted of three flights completed on November 14–15, 2013. 

During these three flights, an F/A-18 structural mode identification test was completed while in 

level flight, as well as 46 SLS-like trajectories in autopilot mode, which were designed to match 

predefined SLS scenarios. The structure of the initial test series was such that at the conclusion 

of the first series the principal AAC design objectives (1 through 3) had been successfully 

demonstrated multiple times. 

During the second flight campaign completed on December 11–12, 2013, the emphasis shifted to 

amplification and recovery of a physical airframe mode using identification data from the first 

campaign, exploring interactions between manual steering and AAC, and repeating SLS 

scenarios that had exhibited in-flight variability. Approximately 13 straight and level tests were 

completed to demonstrate the ability of the AAC to mitigate an intentionally destabilized

airframe structural mode, and 45 SLS-like trajectories were flown across three sorties. 

A brief description of the SLS scenarios flown is provided in Tables 9.1-1, 9.2-1, and 9.3-1. 

TCs completed to explore interactions between manual steering and the AAC are indicated in the 

aforementioned tables. A more complete summary of each sortie is tabulated in Appendix A.

9.1 Flight Test Results for Near-nominal SLS Scenarios (Objective 1)

The first flight objective was intended to show negligible adaptation in an environment that is 

commensurate with the nominal controller design envelope. Three TCs were flown that map 

into this objective, as shown in Table 9.1-1. Each of the TCs was flown with the AAC on and 

off for comparison.

Table 9.1-1. Flight-tested Objective 1 Scenarios/TCs

TC DESCRIPTION AAC FT 1 FT 2 FT 3 FT 4 FT 5

0 Nominal plant, environment, and

controller

OFF A A M(2) M(2)

ON A(3) A(2) M(2) M(2)

1 Heavy/slow vehicle OFF A

ON A

2 Light/fast vehicle OFF A

ON A

“A” – Experiment conducted in autopilot mode; “M” – manual mode (pilot steering commands); 

number in parentheses indicates number of times TC was flown if greater than 1.

In the absence of adaptation, the total adaptive gain multiplier, shown in Figure 9.1-1, would be 

unity. The flight test results are consistent with the MSFC simulation and the AFRC HILS and 

show little adaptation. There is a small but out-of-family decrease in the total loop gain shortly 

after 60 seconds, which was caused by a pitch transient induced by the pilot de-throttle command 

near the trajectory crest. The pitch error and left booster angle (illustrating control usage) are 

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shown in Figure 9.1-2, showing excellent matching across simulations and flight with little 

adaptation. The other test scenarios showed similar results, further demonstrating minimal 

adaptation inside the normal SLS flight envelope and satisfying objective 1.

Figure 9.1-1. Adaptation of the Total Loop Gain (nominal SLS scenario)

Figure 9.1-2. Results for the Nominal SLS Scenario

9.2 Flight Test Results with Low Performance SLS Scenarios (Objective 2)

The second flight objective was to demonstrate improved performance in an environment where 

the nominal controller performance is poor. To generate these scenarios based on SLS 

dynamics, key parameters within the model, controller, and environment were modified to 

create stressing conditions to challenge the fixed-gain control system. Table 9.2-1 contains a 

description of the SLS conditions for each objective 2 test scenario. In several cases, slosh 

dynamics were disabled to isolate the effects of the simulated rigid body or elastic instability. 

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In the cases involving wind disturbances, the SLS aerodynamic instability was exaggerated to 

further stress the baseline attitude controller.

Table 9.2-1. Flight-tested Objective 2 Scenarios/TCs

TC DESCRIPTION AAC FT 1 FT 2 FT 3 FT 4 FT 5

3 Wind shear event OFF A

ON A

4 TVC bias OFF A(2)

ON A

5 Hardover failure of two core engines 

(offset in time)

OFF A M M

ON A M M

6 Heavy/slow, wind shear, SRB tailoff thrust 

imbalance

OFF A

ON A

7 Wind shear event and double hardover 

failure

OFF A M M

ON A M(2) M

14 Low-gain controller, wind shear, two

hardover failures

OFF A

ON A

“A” – Experiment conducted in autopilot mode; “M” – manual mode (pilot steering commands); 

number in parentheses indicates number of times TC was flown if greater than 1.

All of the objective 2 scenarios exhibited excellent matching between the MSFC simulated 

results, AFRC HILS predictions, and the flight tests. This matching is exemplified here through 

the results of TC 7, shown in Figures 9.2-1 and 9.2-2. This SLS-derived scenario features a wind 

shear event followed by two core engine hardover failures. This scenario would have forced the 

SLS without the benefit of the AAC to exceed the structural limits shortly after 60 seconds, at 

which time the TCs are discontinued, as observed in Figure 9.2-2. In the AAC-on scenarios, the 

on-line gain adjustment improves the response time of the controller to the disturbance, keeping

the SLS within the limits of the structural load indicator without requiring excessive control 

commands (see Figures 9.2-1 and 9.2-2). Thus, the AAC algorithm meets the objective of 

increasing the total loop gain in response to excessive error in the pitch attitude beyond what 

would be expected within a normal flight envelope. Other TCs that map to this objective 

produced similar results, thereby increasing the confidence that the AAC improves the SLS 

vehicle performance under anomalous conditions.

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Figure 9.2-1. Adaptation of the Total Loop Gain (low performance scenario)

Figure 9.2-2. Low Performance Results

9.3 Flight Test Results with Unstable Parasitic Dynamics (Objective 3)

The third flight objective was to demonstrate the AAC's ability to restrict unstable, mismodeled 

parasitic dynamics to a small-amplitude, nondestructive limit cycle. The TCs associated with 

this objective are shown in Table 9.3-1. Matching the high-frequency, unstable SLS parasitic 

dynamics with the F/A-18 for objective 3 TCs was more challenging than with tracking the SLS

dynamics for the objective 1 and 2 scenarios. Contributing to the difficulty in matching the highfrequency SLS dynamics is the bandwidth available to the NDI for generating the necessary 

frequency content and magnitude. The NDI is sensitive to the aircraft q, which varied

throughout the maneuver and from trajectory-to-trajectory depending on the initial conditions of 

the maneuver. Thus, the resulting pitch attitude and rate amplitude were a function of the initial 

condition at the time the experiment is engaged. Furthermore, the aircraft inertia properties

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change significantly as a function of fuel level. To better understand these variations and 

provide data for the analysis of the NDI performance for high-magnitude, high-frequency modes, 

each of the scenarios listed was repeated at various initial conditions and aircraft fuel states.

Table 9.3-1. Flight-tested Objective 3 Scenarios/TCs

TC DESCRIPTION AAC FT 1 FT 2 FT 3 FT 4 FT 5

9 Light/fast with slosh instability OFF A A

ON A A

10 Structural instability OFF A(2) A

ON A A

15 High-gain controller, slosh instability OFF A A MA(2)

ON A A MA

16 High-gain controller, unstable flex OFF A A MA(2)

ON A(2) A MA

17 High-gain controller, rigid body 

instability

OFF A A MA(2) MA(2)

ON A A(2) MA(2) MA(2)

19 Identification of F/A-18 structural mode OFF S/L(3) 

20 F/A-18 structural mode amplification with 

INS

OFF S/L S/L(2)

ON S/L S/L

22 F/A-18 structural mode amplification with 

EGI

OFF S/L S/L(4)

ON S/L S/L(2)

“A” – Experiment conducted in autopilot mode; “M” – manual mode (pilot steering commands); 

“S/L” – straight and level flight; number in parentheses indicates number of times TC was flown 

if greater than 1.

Results are shown for TC 16, where the FCS gains were increased and the gain on one of the 

elastic modes was increased to simulate closed-loop instability. The flex dynamics were applied 

to the aircraft via the ailerons, and alternate effectors (i.e., primarily stabilators) were used for 

implementation of the FCS commands. The flaps were mixed in with the stabilators, but to a 

small extent, and are primarily scheduled with α for aircraft trim. Figure 9.3-1 shows the total 

loop gain at its initial value of 1, then decreasing rapidly at approximately 35 seconds when the 

SLS model disturbance was applied to excite the closed-loop instability of the elastic mode. As 

the instability grows, the frequency content enters into the SLS control command and the AAC 

responds by decreasing the loop gain to its limit of 0.5. The vehicle deflection is shown in 

Figure 9.3-2, with the SRB TVC command. The amplitude of the elastic deflection was higher 

during flight than observed in either simulation. This was due to the reduced performance of the 

NDI caused by the aileron-generated moments at the SLS mode frequency being higher than 

predicted by the simulation and assumed by the NDI aerodynamic coefficient tables. The 

resulting larger amplitude instability could not be eliminated with the total loop gain at its 

minimum value. The simulated LOV was delayed for approximately 10 seconds, which was 

considered a significant accomplishment. A similar trend was seen in TC 10, which used the 

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aircraft's ailerons to provide motion corresponding to simulated flex instability. TCs 9, 15, and 

17, which used alternate control surfaces, were able to demonstrate stabilization (i.e., reduction 

to a low-amplitude limit cycle) through the use of AAC.

Figure 9.3-1. Adaptation of the Total Loop Gain (fictitiously unstable, flexible body dynamics)

Figure 9.3-2. Results for the Fictitiously Unstable Flexible Body Dynamics

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9.4 Airframe Structural Mode Identification and Amplification Test 

(Objective 3)

The objective 3 TCs for the majority of the research flights demonstrated stabilization of 

unstable SLS structural modes, slosh, or rigid body dynamics as implemented in the SLS 

reference model within the aircraft's software. These instabilities were driven by control surfaces 

and measured through the resultant aircraft rigid body response. Additional tests were conducted 

to further demonstrate the ability of the AAC algorithm to attenuate unfavorable interaction 

between the control system and the physical F-A/18 structural dynamic response. During the 

first research flight, tests were conducted to identify the primary aircraft pitch structural mode of 

the airframe from the symmetric stabilator command to the pitch rate response at each of the two 

sensor packages. During the second series of flights, TCs were flown so that, using a high-gain 

filter, intentionally destabilized the closed-loop response with the F/A-18 airframe mode and 

demonstrated the ability of the adaptive element to suppress the divergent response. For both 

sets of tests, the baseline F/A-18 filters on the pitch rate feedback to the SLS controller were set 

to unity, while the filters in the FAST NDI feedback paths were maintained. In addition, the SLS 

controller attitude error and rate error filters were set to unity, and the SLS reference model slosh 

dynamics, flex dynamics, and actuator limits were all disabled. These adjustments yielded a 

simple system in which the SLS FCS with adaptive element interacts with the FAST NDI and the 

basic F/A-18 airframe.

To identify the structural response, a 60-second phase-optimized multiple-sinusoid PTI signal

[ref. 18] was added to the SLS angular acceleration control command with a frequency range 

around the expected resonance. The magnitude of the resultant PTI waveform was configurable 

in flight with subsequent presses of the nosewheel-steering button. The highest magnitude of the 

five available values corresponded to the point at which the stabilators were predicted to reach 

actuator rate saturation based on observations during the HILS V&V simulation. The in-flight 

adjustability of the test input magnitude allowed for a buildup approach to ensure the safety of 

the airframe and pilot in the case of unexpectedly high response magnitudes or other unpredicted 

behaviors. For each of the modal identification test segments, the pilot selected the desired PTI 

magnitude as indicated by the control room, engaged the experiment during straight and level 

flight, and thereafter maintained speed and altitude (approximately constant q) with stick 

motions. In total, three 60-second segments were flown, each with increasing magnitude. 

Figure 9.4-1 shows the frequency response as reconstructed from the flight data using the 

second largest magnitude PTI.

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Figure 9.4-1. F/A-18 Airframe Mode Reconstructed Frequency Response

After obtaining the reconstructed flight results, a simple second-order linear model (i.e., 

parameterized by frequency, damping, gain, and delay) of the structural mode was superimposed 

with the rigid body dynamics model and fitted to the flight data. The resulting model (shown by 

lines labeled “model” in Figure 9.4-1) was used to design an eighth-order bandpass filter

[ref. 19] to place in the control path, so the structural mode was amplified to instability, but 

within the capability of the AAC to mitigate the divergent response. The bandpass filter, shown 

in Figure 9.4-2, was placed on the SLS FCS output to avoid amplification of sensor noise into 

the AAC spectral damper and to effect direct frequency shaping of the total system response. To 

allow tuning of the magnitude and phase in-flight, four gain options and five frame delay options 

were applied in series with the amplification filter.

Figure 9.4-2. Airframe Resonance Amplification Filter

For the modal amplification TCs, the bandpass filter was applied, and delay conditions were 

configured in-flight to achieve a structural instability within the AAC capability to recovery. 

During the first test segments of the second phase of research flights, the pilot engaged the 

amplification experiment at the straight and level flight conditions flown during the identification 

tests. 

Figure 9.4-3 shows the results from the amplification tests flown with the AAC disabled and 

enabled. In each case, a short, attenuated burst of the original PTI signal is scheduled at the 

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5-second mark to excite the structural resonance. For the case in which the AAC is disabled, the 

symmetric stabilator response (i.e., black line, left plot) diverges until reaching a limit cycle 

constrained by its maximum actuator rate capability. When the AAC is enabled, the total loop

gain is decreased (i.e., red line, right plot), yielding a reduced control response with significantly 

increased margin from its rate limits. This TC demonstrated the ability of the AAC algorithm to 

recover from an unstable physical structural resonance. Additional details regarding the airframe 

structural mode identification and amplification test are documented in Appendix C.

Figure 9.4-3. Airframe Structural Mode Recovery TC Results

9.5 Evaluation of Manual Steering Mode (Objective 4)

Manual steering is a human-in-the-loop attitude control mode under consideration for the SLS. 

Manual steering allows the crew to have direct, onboard control of the SLS trajectory through the 

FCS after SRB separation. Manual control introduces an additional dimension of uncertainty 

into the robustness analysis of aerospace vehicles, given the ability of the pilot to adjust the 

closed-loop gain of his or her dynamic response [ref. 20]. This variability is especially relevant 

when coupled to adaptive FCSs (e.g., AAC) that adjust the system gain. When the pilot and the 

adaptive system make gain adjustments based on the same feedback parameters (e.g., tracking 

error), the potential exists for adverse interactions [refs. 21–23].

Experimental characterization of this interaction with critical analysis from pilot evaluations may 

assist in determining whether the AAC should remain active if manual steering is engaged in the 

event that the SLS Program decides to employ a manual steering mode for the operational 

vehicle. For this reason, the fourth objective to explore interactions between manual steering and 

the AAC was added to the LVAC experiment. No official SLS manual steering design existed 

prior to the flight experiment, so the test team implemented a prototype design, shown in 

Figure 9.5-1, based on assumed system requirements. It should be noted that the flight test 

results will inform the official design, and that any flaws discovered in the prototype may not be 

present in the final manual steering mode architecture.

During the research flights, the ILS glideslope deviation needle on the attitude reference 

indicator (ARI) displayed pitch attitude error to the pilot. The ARI position at the top of the 

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instrument panel, near the heads-up display (HUD), allowed the pilot to maintain external

situational awareness. Pitch rate control inputs were made through control stick longitudinal 

deflection. The pilot lateral stick and rudder pedals were disabled with the SLS control laws 

engaged. A separate autopilot maintained lateral-directional control.

As shown in Figure 9.5-1, the pilot control input was a rate command proportional to pitch stick 

deflection. This rate command was integrated to allow both rate and attitude errors to be 

computed and used as inputs to the control laws. The trajectory cue displayed to the pilot was a 

fixed –0.75-degree/second nose-down command.

Figure 9.5-1. Manual Steering Mode Implementation

The LVAC experiment gathered data for 25 test trajectories flown under manual steering mode, 

13 with Pilot A on flight 143, and 12 with Pilot B on flight 144. The 25 test trajectories 

consisted of six different TCs covering the three main test objectives. TCs represent extreme 

events outside the SLS design envelope. Table 9.5-1 lists the manual steering mode test matrix.

In most instances, both pilots evaluated a given TC to account for differences in technique and 

rating methodology. Back-to-back evaluations with and without adaptation helped reduce

uncertainties due to atmospheric and aircraft mass property variations. The “nominal plant and

environment” TC was evaluated at the beginning and at the end of each flight to establish a trend 

for pilot adaptation to the task.

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Table 9.5-1. Manual Steering Mode Test Matrix

Objective TC Scenario Description Card AAC On Pilot A

(No. of Test 

Points)

Pilot B

(No. of Test 

Points)

1 0 Nominal plant and environment 5 YES 2 2

6 NO 2 2

2 5 Two-spaced hardover failures 7 NO 1 1

8 YES 1 1

7 Wind shear, two hardover failures 11 NO 1 1

12 YES 2 1

3 15 High gain plus slosh excitation 25 NO -- 1

26 YES -- 1

16 High gain with unstable flex 19 NO 1 --

20 YES 1 --

17 High gain plus rigid body instability 29 NO 1 1

30 YES 1 1

Test pilots rated each configuration using the aircraft pilot-in-the-loop oscillation (PIO) rating 

scale [ref. 24]. Ratings were given immediately following the completion of each test point. 

The aircraft communication system transmitted comments made by the pilot during the test point 

to the ground control room for situational awareness. This audio stream was recorded with HUD 

video for post-flight analysis, from which the pilot comments were transcribed and documented 

in Appendix E.

A more complete discussion and analysis of the manual steering mode test results can be found 

in Appendix D, with a description of the metrics used to evaluate the manual steering mode and 

its interaction with the AAC. In order to explore the interactions between manual steering and 

AAC, scenarios from each of the first three flight objectives were repeated with manual steering 

engaged. A short discussion of the observations for each of the three objectives is provided in 

the following sections.

9.5.1 Objective 1 TC with Manual Steering

An analysis of the manual steering mode for TC 0, “Nominal plant and environment” indicate 

that the pilot and the adaptive controller interact in such a way as to reduce the adaptive gain 

below its ideal value of 1. Even moderately aggressive pilot commands are of high enough 

frequency to be detected as parasitic dynamics by the spectral damper and cause the adaptive 

gain to be lowered. The reduction in forward-loop gain lowers the vehicle responsiveness to the 

pilot’s commands, which may invite more aggressive manual inputs. These inputs further reduce 

the adaptive gain, and the result is a tendency by the pilot to over-command the system with 

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large, aggressive inputs, causing the adaptive gain to decrease to its lower limit. Although the 

test pilots showed an ability to alter their piloting technique to reduce unwanted interactions, the 

system showed higher than desired pilot workload and a strong tendency for pilot-involved 

oscillations in the presence of large tracking errors or external disturbances.

These results underscore a key assumption in the design of the AAC: that the presence of 

parasitic dynamics in the bandwidth of the spectral damper is indicative of a loop gain that is too 

high and that the correct response is to reduce the adaptive gain. In some cases, the use of 

manual steering mode violates this assumption. Although none have been identified for the SLS, 

it is conceivable that other sources of internal or external unmodeled dynamics, for example, 

external guidance or steering algorithms, may cause the AAC to adjust its gain in a way that 

further excites those dynamics or fails to attenuate them.

9.5.2 Objective 2 TCs with Manual Steering 

Two TCs were flown to evaluate manual steering mode under objective 2:

• TC 5: Two spaced hard-over failures

• TC 7: Wind shear and two simultaneous hard-over failures

For TC 5, the tracking performance was equivalent between the automatically and manually 

flown points when the AAC was off. Activation of the adaptive controller improved 

performance of the autopilot but resulted in larger tracking error under manual steering control.

In TC 7, the ideal response of the adaptive gain is to remain near 1 until the failure occurs and 

then increase toward the maximum value of 2. On the first manual steering attempt by Pilot A, 

the adaptive gain oscillated between the minimum and maximum value, making the vehicle 

response unpredictable to the pilot. The high-workload pilot commands are detected by the 

spectral damper and tend to drive the adaptive gain toward its minimum.

The use of manual steering mode in conjunction with the AAC for off-nominal scenarios in 

which control effectiveness is overpredicted (e.g., TCs 5 and 7) lowered tracking performance as 

compared with the autopilot.

• With the AAC off, manual steering mode had about the same error as the autopilot

• With the AAC on, manual steering mode had significantly more tracking error than the 

autopilot

9.5.3 Objective 3 TCs with Manual Steering

Three objective 3 TCs with manual steering mode were evaluated:

• TC 15: High gain controller with slosh excitation (Pilot B)

• TC 16: High gain controller with unstable flex (Pilot A)

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• TC 17: High gain controller with rigid body instability (Pilots A and B)

In TC 15, the autopilot stabilized the slosh mode with and without the adaptive controller. 

However, the control commands of Pilot B, without adaptation, excited the slosh mode into an 

instability that caused the system to violate its internal q-α limit near the 76-second point of the 

trajectory. With the AAC on, Pilot B was able to complete the trajectory with only marginally

worse tracking performance than the automatic system.

In TC 16, the unstable flex mode violated the SLS vehicle deflection limit for all attempts. The 

adaptive controller delayed the limit violation. There was little difference between the autopilot 

and manual steering with the AAC enabled. Without adaptive control, the pilot extended the 

length of the trajectory by approximately 9 seconds as compared with the autopilot without 

AAC.

TC 17 was the only objective 3 manual steering mode evaluation performed by both pilots.

Without adaptive control, the unstable rigid body dynamics caused simulated LOV due to q-α

violations for the autopilot and the manually flown trajectories. With adaptive control, command

inputs by the pilots caused the adaptive gain to reach and maintain its minimum value for the 

duration of the trajectory, whereas under autopilot control the adaptive gain periodically returned 

toward the nominal unity value before being driven lower again.

Manual steering mode, due to the higher frequency content of pilot inputs, resulted in lower 

adaptive gains for cases in which the design gain was too high, but did not result in improved 

tracking performance.

• The use of manual steering mode has the effect of lowering the adaptive gain, which is

beneficial for cases where the vehicle loop gain is too high.

• Certain types of mismodeled dynamics (e.g., slosh modes and rigid body modes) may 

contribute to unstable PIOs. The AAC can help reduce, but not eliminate the likelihood 

of these events.

9.6 Summary of Results

The ability of the SLS FCS to recover from adverse flight conditions using adaptation has been 

conclusively demonstrated through a series of flight tests. Many of the simulated flight 

conditions could not be suitably accommodated in the absence of the adaptive algorithm. The 

AAC functionality was as expected in all scenarios and the flight data matched the SIL and HILS

simulation results in the low-performance and near-nominal TCs. For the objective 3 cases 

exhibiting more deviation from the preflight simulation predictions, the AAC was still shown to 

recover from or significantly delay the consequences of instability. The number of successful 

trajectories demonstrated in this flight test, in addition to the simulated trajectories, advances the 

confidence of the SLS Program toward employing the AAC algorithm.

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Although the research flights demonstrated that AAC can aid the vehicle’s ability to retain stable 

flight control and limit flight loads during certain adverse scenarios, manual steering interactions 

with the AAC as-implemented can increase the probability of an adverse pilot-vehicle 

interaction. Thus, if a manual steering mode is used in subsequent missions, design changes or 

fade-out of the adaptive component upon engagement may be needed. 

10.0 Findings, Observations, and NESC Recommendations

10.1 Findings

The following findings were identified based on the SLS-based scenarios that were flight tested:

F-1. The SLS AAC was confirmed to have minimal impact on the response of the FCS for 

nominal or near-nominal scenarios.

F-2. The SLS AAC was able to consistently enhance the robustness of the fixed-gain FCS 

such that it could compensate for physically realizable launch vehicle scenarios with lowperformance or unstable internal dynamics. 

F-3. Inputs made by the pilot through manual steering during a nominal or low-performance 

launch vehicle scenario tend to suppress the AAC gain below the value that provides the 

best combination of performance and robustness.

F-4. The AAC algorithm can increase the probability of an adverse pilot-vehicle interaction 

if the SLS manual steering mode is engaged during a nominal or low-performance 

scenario.

10.2 Observations

The following observations were identified:

O-1. The manual steering mode evaluated does not reflect the SLS manual steering design as 

this operational mode has not yet been designed.

O-2. The likelihood and severity of adverse interactions between the pilot and the AAC can be 

reduced through modifications in pilot technique.

O-3. Pilot steering did not enhance vehicle performance or robustness beyond what could be 

achieved by the SLS FCS (inclusive of the AAC) although under certain failure scenarios

the pilot could outperform the fixed-gain control system (without the AAC). 

O-4. The F/A-18 FAST aircraft and AFRC engineering and operations personnel are highly

qualified to perform research flights that emulate the dynamics of other aircraft, launch, 

or reentry vehicles in support of GN&C algorithm development and software validation.

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10.3 NESC Recommendations

The following NESC recommendations are directed to the SLS Program. 

R-1. Continue use of the AAC algorithm as part of the SLS FCS pending its maturation 

through standard design cycle analyses, stability analysis, and SIL testing. (F-1, F-2)

R-2. Preclude the use of the AAC algorithm in conjunction with manual steering unless design 

changes are made to mitigate the adverse interactions (e.g., vehicle’s response to pilot 

commands should not be detected as parasitic dynamics by the AAC spectral damper).

(F-3, F-4)

11.0 Alternate Viewpoint

There were no alternate viewpoints identified during the course of this assessment by the NESC 

team or the NRB quorum.

12.0 Other Deliverables

Several versions of the SLS FCS, SLS reference dynamics, and SLS TC scenarios were delivered 

from MSFC to AFRC. Following flight tests, AFRC delivered the logged data to MSFC for each 

flight test scenario as well as the video footage described in Table A-7 in Appendix A. No other 

unique hardware, software, or data packages, outside those contained in this report, were 

disseminated to other parties outside this assessment. 

13.0 Lessons Learned

The following lessons learned were identified for entry into the NASA Lessons Learned 

Information System as a result of this assessment:

• The inclusion of a “spectral damper” in the adaptive update law, which has the effect of 

decreasing the system responsiveness as a result of excitation in the control loop above a 

prescribed cutoff frequency, is a key contributor to simply, consistently, and predictably 

mitigating instabilities resulting from unmodeled or mismodeled internal launch vehicle 

dynamics. This concept may be extensible to other conditionally stable systems.

• The use of NDI allows existing testbeds to mimic the response of a specified dynamic 

system with an equivalent or slower response. This expands the set of applicable testing 

options for flight control software. In the case of the SLS AAC system, the F/A-18 was 

able to provide a cost-effective flight test solution that yielded a large amount of pertinent 

data. 

• The SLS Program, NESC leadership, and Center leadership enabled and encouraged open 

communications across center and contractor boundaries, and empowered the technical 

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staff with a high degree of autonomy. The trust given to the LVAC project team by the 

program leadership contributed to the team’s ability to be aggressive and agile, 

completing all of the milestones on a tight schedule. This high degree of trust from the 

top was reinforced at the technical level between the partner organizations. This project 

would not have been the success it was without these relationships founded on trust and 

mutual respect across all levels of the project team. This relationship was built on and 

maintained by open and transparent communications regarding all aspects of the project,

both positive and negative, without regard for organizational boundaries.

14.0 Recommendations for NASA Standards and Specifications

No recommendations for NASA standards and specifications were identified as a result of this 

assessment.

15.0 Definition of Terms 

Corrective Actions Changes to design processes, work instructions, workmanship practices, 

training, inspections, tests, procedures, specifications, drawings, tools, 

equipment, facilities, resources, or material that result in preventing, 

minimizing, or limiting the potential for recurrence of a problem. 

Finding A relevant factual conclusion and/or issue that is within the assessment 

scope and that the team has rigorously based on data from their 

independent analyses, tests, inspections, and/or reviews of technical 

documentation.

Lessons Learned Knowledge, understanding, or conclusive insight gained by experience 

that may benefit other current or future NASA programs and projects. 

The experience may be positive, as in a successful test or mission, or 

negative, as in a mishap or failure.

Observation A noteworthy fact, issue, and/or risk, which may not be directly within 

the assessment scope but which could generate a separate issue or concern 

if addressed. Alternatively, an observation can be a positive 

acknowledgement of a Center/Program/Project/Organization’s operational 

structure, tools, and/or support provided.

Problem The subject of the independent technical assessment.

Proximate Cause The event(s) that occurred, including any condition(s) that existed 

immediately before the undesired outcome, directly resulted in its 

occurrence and, if eliminated or modified, would have prevented the 

undesired outcome.

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NASA Engineering and Safety Center

Technical Assessment Report

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Recommendation A proposed measurable stakeholder action directly supported by specific 

Finding(s) and/or Observation(s) that will correct or mitigate an identified 

issue or risk.

Root Cause One of multiple factors (events, conditions, or organizational factors) that 

contributed to or created the proximate cause and subsequent undesired 

outcome and, if eliminated or modified, would have prevented the 

undesired outcome. Typically, multiple root causes contribute to an 

undesired outcome.

Supporting Narrative A paragraph, or section, in an NESC final report that provides the detailed 

explanation of a succinctly worded finding or observation. For example, 

the logical deduction that led to a finding or observation; descriptions of 

assumptions, exceptions, clarifications, and boundary conditions. Avoid 

squeezing all of this information into a finding or observation

16.0 Acronym List

α Angle of Attack

σ Standard Deviation

q Dynamic Pressure

A “Autopilot” Mode

AAC Adaptive Augmenting Control

AFRC Armstrong Flight Research Center (formerly Dryden Flight Research Center)

AMA Analytical Mechanics Associates

ARI Attitude Reference Indicator

ARTS Airborne Research Test System

CDR Critical Design Review

CxP Constellation Program

DAC Design Analysis Cycle

dB Decibels

DCA Disturbance Compensation Algorithm

EGI Embedded GPS INS

F/A Fighter/Attack

FAST Full-scale Advanced Systems Testbed

FCS Flight Control System

FRACTAL Frequency Response Analysis and Comparison Tool Assuming Linearity

FT Flight Test

FTP Flight Test Plan

GN&C Guidance, Navigation, and Control

GPS Global Positioning System

GSFC Goddard Space Flight Center

NESC Request No.: TI-13-00847
NASA Engineering and Safety Center

Technical Assessment Report

Document #:

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Version:

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HILS Hardware-in-the-Loop Simulation

HUD Heads-up Display

Hz Hertz

ILS Instrument Landing System

INS Inertial Navigation System

IMU Inertial Measurement Unit

IRT Independent Review Team

LaRC Langley Research Center

LOM Loss of Mission

LOV Loss of Vehicle

LSQI Quadratic Inequality Constrained Least Squares

LVAC Launch Vehicle Adaptive Control

M “Manual” Mode

MSFC Marshall Space Flight Center

MTSO Management and Technical Support Office

NDI Nonlinear Dynamic Inversion

NESC NASA Engineering and Safety Center

NG Northrop Grumman

NRB NESC Review Board

ORD Objectives and Requirements Document

PDR Preliminary Design Review

PID Proportional-Integral-Derivative

PIO Pilot-in-the-Loop Oscillation

PTI Programmed Test Input

RFA Request for Action

RFCS Research Flight Control System

RGA Rate Gyroscope Assembly

S/L “Straight and Level” Flight

SIL Software Integration Laboratory

SLS Space Launch System

SRB Solid Rocket Booster

SRDD Software Requirements and Design Document

STMD Space Technology Mission Directorate

TC Test Case

TN Tail Number

TPM Technical Performance Metric

TRL Technology Readiness Level

TVC Thrust Vector Control

V&V Verification and Validation

VVTP Verification and Validation Test Plan

NESC Request No.: TI-13-00847
NASA Engineering and Safety Center

Technical Assessment Report

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Version:

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17.0 References

1. Orr, J. and VanZwieten, T.: “Robust, Practical Adaptive Control for Launch Vehicles,” 

AIAA-2012-4549, August 2012.

2. Hanson, J.: “A Plan for Advanced Guidance and Control Technology for Second 

Generation Reusable Launch Vehicles,” AIAA-2002-4557, 2002.

3. Wall, J., Orr, J., and VanZwieten, T.: “Space Launch System Implementation of Adaptive 

Augmenting Control,” in AAS Guidance, Navigation, and Control Conference 

Proceedings, Breckenridge, CO, AAS 14-051, 2014.

4. Orr, J., Wall, J., VanZwieten, T., and Hall, C., “Space Launch System Ascent Flight 

Control Design,” in AAS Guidance, Navigation, and Control Conference Proceedings, 

Breckenridge, CO, AAS 14-038, 2014.

5. “Control Algorithm and Parameters for the Ares-I-X Flight Test Vehicle,” AI1-SYSCAP-V4.00, October 2009.

6. Brandon, J., Derry, S., Heim, E., Hueschen, R., and Bacon, B.: “Ares-I-X Stability and 

Control Flight Test: Analysis and Plans,” in AIAA Space 2008 Conference Proceedings, 

San Diego, CA, 2008.

7. “NASA Research and Technology Program and Project Management Requirements, 

Appendix J: Technology Readiness Levels (TRLs),” NPR 7120.8, April 18, 2013.

8. Hanson, C.: “Capability Description for NASA’s F/A-18 TN 853 as a Testbed for the 

Integrated Resilient Aircraft Control Project,” Tech. Rep. DFRC-IRAC-CAP-002, NASA 

Dryden Flight Research Center, January 2009.

9. Miller, C.: “Nonlinear Dynamic Inversion Baseline Control Law: Architecture and 

Performance Predictions,” in AIAA Guidance, Navigation, and Control Conference 

Proceedings, Portland, OR, 2011.

10. Miller, C.: “Nonlinear Dynamic Inversion Baseline Control Law: Flight-Test Results for 

the Full-scale Advanced Systems Testbed F/A-18 Airplane,” in AIAA Guidance,

Navigation, and Control Conference Proceedings, Portland, OR, 2011.

11. Orr, J., Johnson, M., Wetherbee, J., and McDuffie, J.: “State Space Implementation of 

Linear Perturbation Dynamics Equations for Flexible Launch Vehicles,” AIAA-2009-

5962, 2009.

12. Frosch, J. and Vallely, D.: “Saturn AS-501/S-IC Flight Control System Design,” 

J. Spacecraft, Vol. 4, No. 8, 1967, pp. 1003–1009.

13. Orr, J.: “Elastic Model Transitions Using Quadratic Inequality Constrained Least 

Squares,” AIAA 2012-4561, August 2012.

NESC Request No.: TI-13-00847
NASA Engineering and Safety Center

Technical Assessment Report

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Version:

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Title:

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Algorithm on an F/A-18

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14. Jurenko, R., Bush, J., and Ottander, J.: “Elastic Model Transitions: A Hybrid Approach 

Using Quadratic Inequality Constrained Least Squares (LSQI) and Direct Shape Mapping 

(DSM),” in AAS Guidance, Navigation, and Control Conference Proceedings, AAS 14-

034, January/February 2014.

15. Orr, J., Johnson, M., Wetherbee, J., and McDuffie, J.: “State Space Implementation of 

Linear Perturbation Dynamics Equations for Flexible Launch Vehicles,” AIAA 2009-

5962, August 2009.

16. Orr, J.: “FRACTAL Linear Model Dynamics,” BD TCD20090535A, NASA 

MSFC/EV41, April 2009.

17. Orr et al. FCS 2014

18. Klein, V. and Morelli, E. A.: Aircraft System Identification: Theory and Practice, AIAA 

Education Series, 2006.

19. Orr, J.: “Optimal Recursive Digital Filters for Active Bending Stabilization,” in AAS 

Guidance, Navigation, and Control Conference Proceedings, Breckenridge, CO, 2013.

20. McRuer and Krendel, Mathematical Models of Human Pilot Behavior, AGARDograph 

No. 188, January 1974.

21. Thompson, M. O. and Welsh, J. R.: “Flight Test Experience with Adaptive Control 

Systems,” Advanced Control System Concepts, AGARD CP No. 58, AGARD Joint 

Symposium of the Guidance and Control Panel, and the Flight Mechanics Panel of 

AGARD, Oslo, Norway, 3–5 September 1968, pp. 141–147, 1970.

22. Bosworth, J. T. and Williams-Hayes, P. S.: “Flight Test Results from the NF-15B 

Intelligent Flight Control System Project with Adaptation to a Simulated Stabilator 

Failure,” AIAA 2007-2818, 2007.

23. Hanson, C., Schafer, J., Burken, J. J., Johnson, M., and Nguyen, N.: “Handling Qualities 

Evaluations of Low Complexity Model Reference Adaptive Controllers for Reduced 

Pitch and Roll Damping Scenarios,” AIAA 2011-6607, 2011.

24. “Department of Defense Interface Standard: Flying Qualities of Piloted Aircraft,” MILSTD-1797B, pp. 213–214, February 15, 2006.

25. VanZwieten, T., Gilligan, E., Wall, J., Orr, J., Miller, C., and Hanson, C.: “Adaptive 

Augmenting Control Flight Characterization Experiment on an F/A-18,” in AAS 

Guidance, Navigation, and Control Conference Proceedings, Breckenridge, CO, 

AAS 14-052, 2014.

NESC Request No.: TI-13-00847
NASA Engineering and Safety Center

Technical Assessment Report

Document #:

NESC-RP13-00847

Version:

1.0

Title:

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Algorithm on an F/A-18

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18.0 Appendices (separate Volume 2, Parts 1 and 2)

Appendix A. Summary of Research Flights

Appendix B. Complete Set of Results and Discussion for SLS-based Test Cases

Appendix C. F/A-18 Airframe Structural Mode Test

Appendix D. Evaluation of Manual Steering Mode

Appendix E. Pilot In-flight Comments, Flights 143 and 144

Appendix F. FAST Capability/NDI Details

Appendix G. FAST ORD Addendum: LVAC Experiment

Appendix H. FAST SRDD: LVAC Experiment

Appendix I. FAST VVTP: LVAC Experiment

Appendix J. FAST FTP: LVAC Experiment

NESC Request No.: TI-13-00847
REPORT DOCUMENTATION PAGE Form Approved

OMB No. 0704-0188

2. REPORT TYPE 

Technical Memorandum

 4. TITLE AND SUBTITLE

Flight Testing of the Space Launch System (SLS) Adaptive Augmenting 

Control (AAC) Algorithm on an F/A-18

5a. CONTRACT NUMBER

 6. AUTHOR(S)

Dennehy, Cornelius J.; VanZwieten, Tannen S.; Hanson, Curtis E.; 

Wall, John H.; Miller, Chris J.; Gilligan, Eric T.; Orr, Jeb S.

 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)

NASA Langley Research Center

Hampton, VA 23681-2199

 9. SPONSORING/MONITORING AGENCY NAME(S) AND ADDRESS(ES)

National Aeronautics and Space Administration

Washington, DC 20546-0001

 8. PERFORMING ORGANIZATION

 REPORT NUMBER

L-20474 NESC-RP-13-00847

10. SPONSOR/MONITOR'S ACRONYM(S)

NASA

13. SUPPLEMENTARY NOTES

12. DISTRIBUTION/AVAILABILITY STATEMENT

Unclassified - Unlimited

Subject Category 15 Launch Vehicles and Launch Operations

Availability: NASA CASI (443) 757-5802

19a. NAME OF RESPONSIBLE PERSON

STI Help Desk (email: help@sti.nasa.gov)

14. ABSTRACT

The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an adaptive augmenting control 

(AAC) algorithm for launch vehicles that improves robustness and performance on an as-needed basis by adapting a classical 

control algorithm to unexpected environments or variations in vehicle dynamics. This was baselined as part of the Space 

Launch System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was asked to partner with the 

SLS Program and the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCDP) to 

flight test the AAC algorithm on a manned aircraft that can achieve a high level of dynamic similarity to a launch vehicle and 

raise the technology readiness of the algorithm early in the program. This document reports the outcome of the NESC 

assessment.

15. SUBJECT TERMS

NASA Engineering and Safety Center; Adaptive Augmenting Control; Space Launch System; Guidance, Navigation and 

Control; F/A-18

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(443) 757-5802

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